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In the context of an increasing interest towards the safe introduction of technologies that can help human drivers while performing their driving tasks in the ordinary traffic, the proposed work provides a contribution, investigating safety-related aspects of assisted vehicles in a simulated environment, by considering other driving behavioral parameters. In particular, the influence of Adaptive Cruise Control (ACC), combined with driver distraction and route familiarity, on the driving behavior was investigated using a driving simulator. A sample of 37 drivers, aged between 21 and 34 years performed the driving tests in the simulator environment, with different scenarios applied to one road section. The interactions between a lead vehicle and a follower were investigated, collecting the kinematic parameters of the vehicle (speed, acceleration) and its position in the road (lateral position and distance from the lead vehicle) either with or without active ACC conditions. Driving simulation scenarios differed among each other not only for the ACC turned on or off, but also for the secondary tasks presented to the drivers in specific time frames of the test. Moreover, tests were repeated to induce a route familiarity effect and to study its influence on driving behavior, thus on safety. Results highlighted that the active ACC was correlated with more cautious behaviors, in terms of speed, deceleration/acceleration and distance from the lead vehicle. Women drivers were associated to a greater variability in the driving behavior. The visual distraction induced by secondary tasks appeared to have negative effects on safety-related driving performances, especially when the secondary task was highly demanding. On the other hand, when drivers became familiar with the route, they performed more aggressive driving patterns. [ABSTRACT FROM AUTHOR]

This work investigates the fuel efficiency potential of Adaptive Cruise Control (ACC) systems, focusing on two optimization-based control approaches for internal combustion engine (ICE) vehicles. In particular, this study compares two model predictive control (MPC) designs. In the first approach, a strictly quadratic cost is adopted, and fuel consumption is indirectly minimized by adjusting the weights assigned to state tracking and control effort. In the second approach, a fuel consumption map is explicitly included in the MPC cost function, aiming to directly minimize it. Both approaches are compared to a globally optimal benchmark obtained with dynamic programming. Although these methods have been discussed in the literature, no systematic comparison of their relative performance has been conducted, which is the primary contribution of this article. The results demonstrate that, with proper tuning, the simpler quadratic approach can achieve comparable fuel savings to the approach with explicit fuel consumption minimization, with a maximum variation of 0.5%. These results imply that the first alternative is more suitable for online implementation, due to the more favorable characteristics of the associated optimization problem. [ABSTRACT FROM AUTHOR]

This paper presents a formulation and analysis of a low computation Predictive Functional Control (PFC), which is a simplified version of the more advanced Model Predictive Control (MPC) for an Adaptive Cruise Control (ACC) system by using a representation of first order closed-loop transfer function. In this work, a non-linear mathematical model of vehicle longitudinal dynamics is considered as a control plant. Then, a simple Proportional Integral (PI) controller is employed as an inner loop to identify the first-order relationship between its actual and desired trajectory speed according to the reasonable time constant based on the logical response of pedals pressing. To directly control the whole plant, the PFC is formulated as an outer loop to track the desired speed together with the convergence rate based on a user preference while satisfying constraints related to acceleration and safe distancing. Since PFC is formulated based on the firstorder transfer function, the prediction and tuning processes are straightforward and specific to this system. The simulation results confirm that the proposed controller managed to track the desired speed while maintaining a comfortable driving response. Besides, the controller also can retain safe distancing during the car following application, even in the presence of unmeasured disturbance. In summary, this framework can avoid the need to formulate an inverse non-linear model that is typically used when deploying a hierarchical control structure to compute the throttle and brake pedals pressing as it has been replaced with an inner loop PI controller. The performance also is comparable yet more conservative due to the simplification. These findings can become a good reference for designing and improving the ACC controller, as the framework can be easily generalized for any type of vehicle for future work. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The evolution of autonomous vehicles hinges significantly upon the advancements in driving assistance systems. Adaptive cruise control, a pivotal component of these systems, warrants continuous real-world examination to assess its operational efficiency. The study investigates these systems integrated into diverse commercial vehicles with a specific focus on the following distances they provide. The findings reveal that camera-based systems offer shorter following distances relative to ISO standards, while radar-based and combined camera and radar-based systems provide larger following distances. The study contributes to understand adaptive cruise control technology and its alignment with safety standards, thereby aiding in the on-going development of self-driving vehicles. [ABSTRACT FROM AUTHOR]

The intelligent assistant driving system can ensure driving comfort at a safe driving distance. The longitudinal control system mainly determines the longitudinal movement of the vehicle at the safe driving speed. The purpose of this study is to accelerate the maturity of automatic vehicle driving system, shorten the response time of control strategy and improve its comfort. Based on the automatic speed control strategy, a driving vehicle test system is designed. Scene extraction, noise removal and working condition reorganization are carried out from the longitudinal control strategy. The performance analysis and experimental test under layered test evaluation are conducted from the scenes of constant speed cruise, target vehicle stationary, target vehicle low speed, target vehicle deceleration etc. The results show that the proposed scene extraction algorithm is 42.86% different from the traditional algorithm in acceleration comparison. The initial braking distance of the algorithm is 0.80 m. The safe triggering distance is the minimum (<15m). The maximum deceleration of braking and stopping at constant speed is -0.65 g, which has better driving comfort and safety and reduces the risk of collision. The automatic test system can effectively ensure driving safety, driving accuracy and convenience in different driving scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback–Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects. [ABSTRACT FROM AUTHOR]

Adaptive Cruise Control (ACC) systems have emerged as a significant advancement in automotive technology, promising safer and more efficient driving experiences. However, the performance of ACC systems can vary significantly depending on their type and underlying algorithms. This research presents a comprehensive comparative analysis of car-following distances in different types of Adaptive Cruise Control systems. We evaluate and compare three distinct categories of ACC systems using three different commercial vehicles brands. The study involves extensive real-world testing at Zalazone Proving Ground, to assess the performance of these systems under various driving conditions, including driving at multiple speeds and applying different car following scenarios. The study investigates how each ACC system manages the minimum following distances according to the type of ACC sensors in each tested vehicle. Our findings revealed that at low to medium ranges of constant driving speeds, there was an approximate linear increase in the average clearances between the two following vehicles for all applied scenarios, with comparatively shorter clearances obtained by the vision-based ACC system, while unstable measurements with a high level of dispersion for all ACC systems were observed at high range of driving speeds. [ABSTRACT FROM AUTHOR]

Traditional methods for tracking the paths of driverless vehicles use plant models to determine the corresponding control laws. Due to the intricate interactions between the road and the tires, time-varying characteristics, and unidentified disturbances. It is challenging to create an accurate vehicle model. As a result, data-driven controllers, which are independent of a predetermined plant model are becoming more and more well-liked. This work implements adaptive cruise control (ACC) by employing a control approach called extremum seeking technique (EST), which is a model-free control (MFC), to control a vehicle braking and longitudinal acceleration. The main aim here is to create an ego vehicle that travels at a specific speed with maintaining a secure space with respect to a guide vehicle. A car including an ACC technique called ego car, exploits radar to determine relative velocity and relative space relating to the guiding car. The ACC technique is considered to keep maintain a relatively secure space or a preferred cruising velocity concerning the guiding vehicle. The developed model succeeded to determine the relative velocity and relative space according for the ego car to another guiding car with acceleration not more than ±2 m/s2 and spacing error less than 6 m. [ABSTRACT FROM AUTHOR]

As the automotive industry transitions towards the era of autonomous vehicles, it is imperative to assess and compare the following distances maintained by vehicles equipped with adaptive cruise control (ACC) systems against those of traditional human-driven vehicles. This study aims to provide insights into the future use of autonomous vehicles by empirically examining the following distances achieved under different driving conditions. Controlled experiments were conducted using three vehicles equipped with various types of ACC sensors, and comparable scenarios were replicated with human drivers. The experiments involved driving at multiple constant speeds to evaluate the efficacy of ACC in maintaining safe following distances. Our findings indicate that ACC systems consistently converge on optimal following distances, demonstrating their ability to regulate spacing between vehicles effectively. However, a notable downside emerged in terms of their adverse impact on road capacities, where the results indicate a mitigation in capacity percentages of 7.6%, 9.3%, and 15.6% for the three types of ACC-equipped vehicles compared to human drivers. This study sheds light on the intricate interplay between ACC systems and human driving behaviors, emphasizing the need to consider both factors when envisioning the future of autonomous vehicles. While ACC systems provide a standardized and reliable approach to following distances, the shorter distances observed in human-driven scenarios suggest a potential trade-off between safety and traffic capacity. These insights contribute to a comprehensive understanding of the dynamics involved in autonomous driving, facilitating informed decision making for the integration of autonomous vehicles into future transportation systems. [ABSTRACT FROM AUTHOR]

This article will focus on adaptive cruise control in autonomous automobiles. The adaptive cruise control inputs are the safety distance which determines thanks to conditions set depending on the distance value, the measured distance, the longitudinal speed of the autonomous automobile itself, the output is the desired acceleration. The objective is to follow the vehicles in front with safety, according to the distance measured by the ultrasonic sensor, and maintain a distance between the vehicles in front greater than the safety distance which we have determined. For this, we used super twisting sliding mode controller (STSMC) and non-singular terminal sliding mode controller (NTSMC) based on neural network applied to the adaptive cruise control system. The neural network is able to approximate the exponential reaching law term parameter of the NTSMC controller to compensate for uncertainties and perturbations. An autonomous automobile adaptive cruise control system prototype was produced and tested using an ultrasonic sensor to measure the distance between the two automobiles, and an Arduino board as a microcontroller to implement our program, and four DCs motors as actuators to move or stop our host vehicle. This system is processed by code and Simulink Matlab, the efficiency and robustness of these controllers are excellent, as demonstrated by the low longitudinal velocity error value. The safety of autonomous vehicles can be enhanced by improving adaptive cruise control using STSMC and NTSMC based on neural network controllers, which are chosen for their efficiency and robustness. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) is recognized as an effective method to improve vehicle safety and reduce driver workload. This paper proposes a whole hierarchical multi-level state ACC system. According to the function of ACC system, the three-level state ACC system is designed and the conversion mechanism between different states is put forward. As for the complex car-following mode, considering the variable headway safety distance and the road adhesion coefficient, the expected safety distance model is established, using the distance error and the speed error as fuzzy input, based on the fuzzy control algorithm, the following mode is obtained; considering vehicle safety, tracking capability and ride comfort, the control objectives are formulated into the model predictive control algorithm. A dynamic weight strategy is proposed to solve time-varying multi-objective control problems, where the weight can be adjusted with respect to different following conditions. The simulation results demonstrate that the car following performance of ACC with the proposed dynamic weighted MPC can provide better performance than that using constant weight MPC, and the multi-level state ACC system can display the control mode more intuitively. [ABSTRACT FROM AUTHOR]

This abstract serves as a concise yet comprehensive overview of this research's contributions, highlighting its significance in advancing Adaptive Cruise Control technology and autonomous vehicles. The provided paper introduces an innovative approach to Adaptive Cruise Control systems, emphasizing safety, comfort, and efficiency. Also, the proposed Adaptive Cruise Control model surpasses traditional longitudinal velocity control by integrating lateral motion and surface condition considerations. The proposed control strategy uses a new tail-following approach with the implementation of a new throttle valve controller which results in a smoother deceleration with an average of 40 percent decrease in maximum deceleration value while following other vehicles. Also, the implementation of brakes is minimized to lower the overall energy waste in vehicle motion. The proposed Adaptive Cruise Control can regulate braking force and tail-following distance based on road surface material and circumstances. This action enhances safety while driving on various roads and weather conditions. One of the innovative sections in this study is the integration of lateral motion with Adaptive Cruise Control. This approach helps the vehicle to stay laterally stable by limiting the lateral acceleration of the vehicle. The research signifies a notable advancement in Adaptive Cruise Control technology, establishing a connection between vehicle dynamics and adaptive control algorithms. [ABSTRACT FROM AUTHOR]

To maintain a safe distance between the autonomous vehicle and the leader, ensure that the vehicle runs at its expected speed as far as possible, and achieve various control requirements such as speed, distance and collision avoidance, a model-free prescribed performance adaptive cruise control (ACC) algorithm based on funnel control is proposed. The contributions of this paper are that the designed ACC algorithm only requires the speed and position information and can constrain their tracking errors within a predetermined range. When the follower is far away from the leader, the speed-prescribed performance controller adjusts the follower vehicle's speed to the reference velocity. When the follower vehicle approaches the leader vehicle, a distance-prescribed performance controller is designed to adjust the distance between the follower and the leader. On this basis, the prescribed performance function can expand the switching interval, thereby improving the robustness of the speed and distance control switching process. The effectiveness of the designed algorithm is demonstrated in three scenarios, such as approaching and following, emergency braking, and frequent starting and stopping. The results show that during the speed control stage, the designed algorithm allows the vehicle's operating speed to vary within a predetermined spatial range; in the distance control stage, the designed algorithm strictly limits the distance error within the preset range. The speed and distance of the vehicle change smoothly, and there is no overshoot during the initial state adjustment, emergency braking, and frequent start and stop stages, demonstrating a good control effect. [ABSTRACT FROM AUTHOR]

This paper shows the modelling and implementation of an adaptive cruise control (ACC) system for intelligent vehicles using fuzzy logic control approach. Initially, MATLAB Simulink is utilized to design an advanced vehicle model that takes into account most of the vehicle parameters using Simscape Driveline toolkit. Then, the fuzzy logic toolbox in MATLAB Simulink is introduced for designing and simulation of the fuzzy logic system. The proposed ACC algorithm functions in two different modes, the distance and velocity modes, based on the speed of the moving vehicle and the vehicle ahead. In distance control mode, the vehicle measures the actual distance to the vehicle ahead and compares it to the safe distance. If the measured distance is larger than the safe distance, the setpoint will be the safe distance and the system will work on maintaining the actual distance equal or greater than the safe distance. However, in speed control mode, the controller will operate according to the set speed adjusted by the driver given that the safe distance condition is met. This gives the vehicle the ability to make decisions relaying on both the set speed by the driver and the actual distance to the upfront objects. It is worth to mention that only a single controller is employed for both modes. According to MATLAB simulations, it is proven that the designed ACC algorithm using fuzzy logic controller is capable of retaining the vehicle in desired constraints as well as achieving satisfactory results owing to the simplicity of the proposed approach. The findings further demonstrate that the system have actually no overshoot with absolutely null steady state error while responding to the given speed with quite swift rising and settling times. However, there happen to be some rapid fluctuations in the throttle and brake values especially when the actual distance suddenly drops below the desired safe distance which may cause some driving inconvenience to the passengers. [ABSTRACT FROM AUTHOR]

This paper presents a novel model predictive adaptive cruise control strategy of intelligent electric vehicles based on deep reinforcement learning algorithm for driver characteristics. Firstly, the influence mechanism of factors such as inter-vehicle distance, relative speed and time headway (THW) on the driver's behavior in the process of car following is analyzed by the correlation coefficient method. Then, the driver behavior in the process of car following is learned from the natural driving data, the car following model is established by the deep deterministic policy gradient (DDPG) algorithm, and the output acceleration of the DDPG model is used as the reference trajectory of the ego vehicle's acceleration. Next, in order to reflect the driver behavior and achieve multi performance objective optimization of adaptive cruise control of intelligent electric vehicles, the model predictive controller (MPC) is designed and used for tracking the desired acceleration produced by the car following DDPG model. Finally, the performance of the proposed adaptive cruise control strategy is evaluated by the experimental tests, and the results demonstrate the effectiveness of proposed control strategy. [ABSTRACT FROM AUTHOR]

Road accidents are one of the major cause of human deaths around the globe. In order to accelerate autonomous driving, a problem of designing suitable lateral and longitudinal drive control scheme, with an obstacle avoidance mechanism has been considered. For this purpose, a two-wheel equivalent model of a vehicle has been adopted and steering angle velocity is treated as a control input. A cascaded architecture for Obstacle Avoidance and Drive Control has been presented. Impact point algorithm has been developed for Obstacle Avoidance System that receives data from various onboard sensors, and computes potential impact probability based on position and velocity of own vehicle and potential static and moving obstacles. The reference heading and speed signals are continuously updated by the Obstacle Avoidance System. Feedback linearization based lateral and longitudinal drive control laws have been proposed to control the required heading angle and longitudinal speed, respectively. Performance of the proposed control scheme for various operational scenarios has been evaluated through simulations. [ABSTRACT FROM AUTHOR]

Under the trend of vehicle intelligentization, many electrical control functions and control methods have been proposed to improve vehicle comfort and safety, among which the Adaptive Cruise Control (ACC) system is a typical example. However, the tracking performance, comfort and control robustness of the ACC system need more attention under uncertain environments and changing motion states. Therefore, this paper proposes a hierarchical control strategy, including a dynamic normal wheel load observer, a Fuzzy Model Predictive Controller and an integral-separate PID executive layer controller. Firstly, a deep learning-based dynamic normal wheel load observer is added to the perception layer of the conventional ACC system and the observer output is used as a prerequisite for brake torque allocation. Secondly, a Fuzzy Model Predictive Control (fuzzy-MPC) method is adopted in the ACC system controller design, which establishes performance indicators, including tracking performance and comfort, as objective functions, dynamically adjusts their weights and determines constraint conditions based on safety indicators to adapt to continuously changing driving scenarios. Finally, the executive controller adopts the integral-separate PID method to follow the vehicle's longitudinal motion commands, thus improving the system's response speed and execution accuracy. A rule-based ABS control method was also developed to further improve the driving safety of vehicles under different road conditions. The proposed strategy has been simulated and validated in different typical driving scenarios and the results show that the proposed method provides better tracking accuracy and stability than traditional techniques. [ABSTRACT FROM AUTHOR]

This work describes the design of an adaptive cruise control (ACC) applied to a realistic automotive simulation model, considering both upper- and lower-level controllers. The upper-level controller provides a desired acceleration/deceleration to a host vehicle to maintain a safe distance related to a leader vehicle or to track a desired cruise speed otherwise. On the other hand, the lower-level controller provides control signals to the throttle and brake pedals of the host vehicle aiming to track the desired acceleration/deceleration setpoint from the upper-level controller. For the upper-level controller, we consider a control framework that unifies the stability/tracking objective, expressed as a control Lyapunov function (CLF), the safety constraint, expressed as a control barrier function (CBF), and the comfort constraint, by means of a quadratic programming (QP), where safety must be prioritized. The lower-level controller is implemented considering a model-free control. The results, obtained by numerical simulations, demonstrate that the safe distance between the vehicles is ensured and the desired cruise speed is tracked adequately. Therefore, the stability/tracking objective and the safety and comfort constraints, related to the upper-level controller, are properly satisfied. The lower-level controller tracks the desired acceleration/deceleration demanded by the upper-level controller with good performance. [ABSTRACT FROM AUTHOR]

An adaptive cruise control (ACC) system can improve safety and comfort during driving by taking over longitudinal control of the vehicle. It requires the coordination between the upper-layer controller and the lower-layer actuators. In this paper, a hierarchical anti-disturbance cruise control architecture based on electronic stability control (ESC) system is proposed. The upper-layer controller outputs the desired longitudinal acceleration or deceleration to the lower-layer actuators. In order to improve the accuracy of model prediction and achieve the coordinated control of multiple objectives, an upper-layer model prediction cruise controller is established based on feedback control and disturbance compensation. In addition, based on the hydraulic control unit (HCU) model and the vehicle longitudinal dynamics model, a lower-layer nonlinear model predictive deceleration controller is proposed in order to solve the problems of pressure fluctuations and the low accuracy of small decelerations when ESC is used as the actuator for the ACC system. Finally, the simulation and experimental tests were carried out. The results show that the proposed control architecture can improve the stability and comfort of the cruise control process. Moreover, compared with the traditional PID deceleration controller, it effectively improves the deceleration control accuracy. [ABSTRACT FROM AUTHOR]

Vehicles in the adjacent lane making abrupt lane changes is a common and frequent action during traffic movement. Being aware of adjacent vehicles ahead of time, determining their cut-in intention, monitoring their cut-in trajectory in real time, and actively adjusting following speed are all critical for adaptive cruise systems for vehicles. This study proposes a flexible following-factor-calculation approach that considers the driver's willingness to take risks for the purpose of identifying cut-in intent, predicting trajectory, and narrowing the window for following cruise speed adjustment to improve passenger ride comfort. To begin, a lane-change trajectory prediction algorithm based on driver adventitious factor correction is proposed in order to correctly predict the lane-change trajectory of adjacent vehicles in urban road traffic scenarios. Second, the flexible following factor and the flexible switching factor of the following target are constructed to overcome the influence of the uncertainty caused by internal and external disturbances on the vehicle following the motion process, and to reduce the impact of cut-in events on passenger comfort. An anti-disturbance rejection control and an adaptive cruise controller based on the vehicle's longitudinal inverse dynamics model are proposed in order to compensate for and suppress the internal perturbations caused by the vehicle's internal parameter changes and the random disturbances caused by external road environment changes. The results of simulation and real-world testing showed an average of 28% improvement in passenger comfort. [ABSTRACT FROM AUTHOR]

Sensing, computing, and communication advancements allow vehicles to generate and collect massive amounts of data on their state and surroundings. Such richness of information fosters data-driven decision-making model development that considers the vehicle's environmental context. We propose a data-centric application of Adaptive Cruise Control employing Deep Reinforcement Learning (DRL). Our DRL approach considers multiple objectives, including safety, passengers' comfort, and efficient road capacity usage. We compare the proposed framework's performance to traditional ACC approaches by incorporating such schemes into the CoMoVe framework, which realistically models communication, traffic, and vehicle dynamics. Our solution offers excellent performance concerning stability, comfort, and efficient traffic flow in diverse real-world driving conditions. Notably, our DRL scheme can meet the desired values of road usage efficiency most of the time during the lead vehicle's speed-variation phases, with less than 40% surpassing the desirable headway. In contrast, its alternatives increase headway during such transient phases, exceeding the desired range 85% of the time, thus degrading performance by over 300% and potentially contributing to traffic instability. Furthermore, our results emphasize the importance of vehicle connectivity in collecting more data to enhance the ACC's performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering the perception of a driver, driving safety, and riding comfort, an adaptive cruise control (ACC) method based on a multi-objective (MO) real-time optimization control algorithm is proposed. The ACC system can be divided into various working modes according to the driver's perception, as well as the relative state of the host vehicle and the preceding vehicle. To distinguish the driver's intention in the rapid acceleration, strong deceleration, steady, and larger clearance modes, a fuzzy tool is proposed to solve the problem. To maintain the inter-clearance, velocity, and jerking action in a reasonable region, an MO control algorithm based on model predictive control was used to obtain the optimal control vectors. Moreover, the control vector increment is restricted to suppressing the fluctuation caused by switching among various modes. Simulations were conducted for various scenarios to verify the effectiveness of the ACC method. The simulation results indicated that the driver's comfort and fuel economy improved by 35.3% and 16.6%, respectively, compared with the linear quadratic algorithm in a complicated driving environment. Various simulation results demonstrated that the MO-ACC controller can reduce fuel consumption while barely sacrificing riding comfort or tracking performance, as compared to the linear quadratic controller. [ABSTRACT FROM AUTHOR]

• Basic (owner's manuals) and comprehensive (+responsibilities) ACC training was provided. • Older and younger adults differed in their ACC usage after training. • Older adults reported less dynamic situational trust and higher workload than younger drivers. • However, comprehensive training reduced the difference between younger and older adults. • Training on driver support system is connected to the acceptance of FAVs. With the development of driver support systems (SAE Levels 1 – 2), drivers must take on new monitoring and supervision tasks in additional to manual driving. Training is necessary to clarify drivers' new roles and promote safe usage and trust in these systems. Providing training for lower-levels of automation may also benefit drivers' acceptance of future Fully Automated Vehicles (FAVs, SAE Level 5). However, younger and older drivers differ in training preferences (e.g., owner's manual vs on-road trial and error) and hold different attitudes towards automation. This study investigates the effects of additional training on drivers' roles and responsibilities when using Adaptive Cruise Control (ACC, SAE Level 1) for younger and older drivers. Thirty-nine adults (20 younger + 19 older) were trained on one of two ACC training protocols: basic (system functionality, operational procedures, and limitations) and comprehensive (basic training + ACC background and roles of responsibilities). Participants' situational trust and ACC usage was evaluated before, during, and after experiencing an emergency event while using ACC in a driving simulator study. Results showed that the comprehensive training promoted drivers' situational trust in ACC, ACC usage, and the acceptance of FAvs Compared to younger drivers, older drivers used ACC less, reported less dynamic situational trust, higher levels of workload, and lower acceptance. Overall, comprehensive training resulted in older drivers behaving similarly to younger drivers. The comprehensive training also promoted the acceptance of FAVs for both younger and older drivers. In conclusion, training of drivers' roles and responsibilities has an impact on drivers' usage of ACC and may be particularly useful for older drivers. [ABSTRACT FROM AUTHOR]

This paper proposes an economic adaptive cruise controller (EACC) that considers battery aging characteristics based on adaptive model predictive control (AMPC). By establishing a battery capacity decay model based on experimental data, the capacity loss during vehicle operation is determined, and the parameters in the equivalent circuit model are updated according to the actual capacity of the battery. The controller uses indicators that characterize driving safety, tracking performance, comfort, and economy. The economic indicator is the decrease in the value of the battery capacity. Fuzzy weight allocation is designed based on the host vehicle's speed and the workshop's relative distance to adjust the weight between different indicators under different working conditions. Additionally, the proposed controller is compared with other traditional controllers under different working conditions, cycle times, and battery state of health (SOH). The simulation results indicate that, under various battery SOH conditions, the performance of the controller which considers battery capacity degradation characteristics is better than that of traditional controllers. Moreover, the fixed-weight controller performs better when following a vehicle at medium and low speeds. Finally, the proposed strategy was validated through hardware-in-the-loop testing, demonstrating its ability to meet the real-time requirements of the system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Adaptive Cruise Control (ACC) is a popular feature for long-distance highway driving due to its convenience. Research has been conducted on the driving characteristics of commercial ACC vehicles that could impact road capacity and congestion. While response time and string stability are major characteristics, previous studies have often overlooked their variations across driving situations. This study analyzes the dynamic characteristics of commercial ACC, including response time, string stability, and asymmetric behavior across different driving situations. A method is proposed to extract the response time of commercial ACC vehicles during cruising, decelerating, and accelerating situations, using cross-correlation and acceleration threshold methods. Phenomena that influence string stability are categorized based on driving situations focusing on their origin and features. This study identifies patterns in asymmetric behavior and presents a car-following model calibration process that incorporates observed features using the OpenACC dataset. The findings reveal distinct variations in response time across different driving situations, escalating in the sequence of deceleration, cruising, and acceleration. String instability during deceleration is influenced by the vehicle's response time, while during acceleration, it stems from an expanded gap reduction process. ACC vehicles exhibit asymmetric behavior, with a reduced tolerance for gap changes. The Helly model, which integrates response times, asymmetric behavior, and maximum acceleration, accurately simulates vehicle movement and string instability. The observed variations in response time and asymmetric behavior across driving situations provide an understanding of the traffic hysteresis of commercial ACC vehicles. Furthermore, our analysis suggests that achieving string stability requires diverse approaches for each driving situation. • Proposes a method to extract ACC vehicle response times, revealing variations across driving situations. • Identifies causes of string instability by distinguishing acceleration and deceleration. • Highlights ACC vehicles' asymmetric behavior, contrasting it with that of human drivers. • Observed traits-based model better emulates ACC vehicle movement and string instability. [ABSTRACT FROM AUTHOR]

• The typical functional forms and consequences of sensing attacks are reviewed. • Stochastic driving behavior is considered in numerical experiments. • Gained insights could guide the design of sensing attack detectors to ensure traffic safety across varying ACC rates. Mixed traffic environments combining human-driven vehicles (HDVs) and those equipped with Adaptive Cruise Control (ACC) have already become prevalent. This study tackles the critical yet underexplored threat of sensing attacks, such as jamming and spoofing, on ACC systems. By applying stochastically calibrated ACC and HDV car-following models grounded in field data, we constructed an integrated and high-fidelity framework to simulate mixed traffic. This allows us to comprehensively analyze traffic safety risks enabled by surrogate safety measures, under various sensing attack scenarios and considering mechanisms for cyberattack detection and human intervention. Our findings highlight profound vulnerabilities in traffic safety from sensing attacks, with factors including stochastic driving behaviors, ACC penetration rates, and attack effectiveness. Through scenario-based sensitivity analyses, this research underscores the potential risks more realistically by stochastic simulation and also contributes to the design of detection systems to safeguard mixed traffic. Ultimately, this work provides valuable insights into evaluating the robustness of ACC systems against sensing attacks, supporting the ongoing and future development of effective countermeasures. [ABSTRACT FROM AUTHOR]

• The work unveils the existence of multi-leader anticipation in commercial vehicles. • Simulations and tests are performed to assess the multi-anticipation safety benefits. • The specific multi-anticipation proves effective in reducing the risk of collision. • Minor impacts on traffic flow as the mechanism is used as an active safety system. Extensive experimental analyses concerned with Adaptive Cruise Control (ACC) have clearly shown that such systems have failed to deliver the promise of safe and traffic-flow effective car-following. On the contrary, large reaction times and poor string stability performances characterize commercial ACCs. While a huge research line is investigating the introduction of communication among vehicles to overcome the mentioned limitation, market adoption of connectivity-enhanced vehicles is struggling. In this context, an alternative approach based on multiple vehicle anticipation using RADAR only has emerged. Multianticipation is definitely not a new concept within the transportation community. However, until now, it was mainly associated with human driving. In the present manuscript, we demonstrate instead how, at least, one vehicle manufacturer has implemented multianticipation on a commercial vehicle. Following an in-house carried out testing campaign, we give an experimental characterization of the functioning of such a system including the potential impact on the flow and safety using a state-of-the-art fuzzy-logic safety performance model. The first results demonstrate that the vehicle under test reacted to one additional vehicle in front of the leader vehicle. Moreover, the actual realization appears to mainly target safety applications whereas there is only a marginal benefit on the string stability characteristics of the system. While we recorded a marginal string stability improvement (about 10 %), the minimum TTC was twice as large when multianticipation occurred with respect to the cases when that was not activated. Relevant Fuzzy Surrogate Safety Metrics further supported the safety argument. [ABSTRACT FROM AUTHOR]

• CAV platoon systems with a novel CACC mode developed to enable stability and robustness under communication disruption. • Delay-robust ACC and CACC modes based on the delay compensating policy and Smith predictor proposed. • Local and string stability conditions of the proposed system in normal and delay-robust ACC and CACC modes derived. • Numerical analysis conducted to verify better stability and safety performances against communication disruption. Connected and automated vehicle platooning systems like cooperative adaptive cruise control (CACC) have shown great potential on improving traffic performance with a shortened time gap through advanced sensing and Vehicle-to-Vehicle (V2V) communication technologies. However, V2V communication is not always reliable in realistic traffic conditions. When V2V communication is disrupted, the degradation of the CACC vehicle will occur due to the loss of feedforward information from the preceding vehicle, and thus the affected vehicle has to switch its control from CACC to adaptive cruise control (ACC) mode. In this study, we propose a novel platooning system with guaranteed stability and robustness against degradation due to communication disruption, in which the linear controllers of ACC and CACC modes are jointly formulated in a unified control framework. With such a proposed platooning system, a relatively small time gap is required for ensuring local and string stability of the two modes to enable robustness against degradation, compared with that of the state-of-the-art platooning systems. To expand the application in practical scenarios, we further develop the delay-robust controllers of ACC and CACC modes based on the delay compensating policy and Smith predictor to maintain better stability and robustness of the proposed platooning system with information delays. Moreover, the sufficient conditions of local stability and string stability in the frequency domain are derived based on the Routh-Hurwitz criterion and Laplace transform, respectively, for the proposed platooning system in normal and delay-robust ACC and CACC modes. Numerical experiments are conducted to demonstrate the mathematical proofs of stability analysis and the performance of the proposed platooning system on stability and safety aspects compared with the baseline platooning system. [ABSTRACT FROM AUTHOR]

Nowadays, due to traffic jam and many cars on traffic, it is very necessary to control the distance between cars and obstacle. Many car producers have been designed and manufacture Cruise Control Systems for cars. Reinforcement learning, one of the popular artificial intelligence techniques, is a method used to train autonomous systems in many different fields. In this simulation study, the adaptive cruise control (ACC) of a ring bus serving in the campus area is controlled with Deep Deterministic Policy Gradient, which is one of the reinforcement learning methods. This simulation study is carried out considering the speed limit in the campus area and the acceleration values required for a comfortable journey of the passengers. Acceleration, velocity and distance values are given with graphs. Consequently; the proposal neural predictor has superior performance to adapt and predict the distance, velocity and acceleration of ego vehicle (bus). [ABSTRACT FROM AUTHOR]

Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

The paper deals with the design of nonlinear adaptive cruise controllers for vehicular platoons operating on an open road or a ring road. The constructed feedback controllers are nonlinear functions of the distance between successive vehicles and their speeds. It is shown that the proposed novel controllers guarantee safety (collision avoidance), speed positivity, bounded acceleration, speed limits by explicitly characterising the set of allowable inputs and by leveraging barrier function techniques. Moreover, we guarantee global asymptotic stability of the platoon to the desired configuration, as well as string stability. Certain macroscopic properties are also investigated. The main challenges stem from the fact that all previous objectives must be satisfied simultaneously and the fact that the control systems studied do not evolve in a finite-dimensional linear space but rather on specific sets. The efficiency of the nonlinear adaptive cruise controllers is demonstrated by means of numerical examples. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC), which is an extension of conventional cruise control, has been applied in many commercial vehicles. Traditional ACC is controlled by proportional-integral-derivative control or linear quadratic regulation (LQR), which can provide sufficient performance to follow a preceding vehicle. However, they can also cause excessive acceleration and jerk. To avoid these excessive behaviors, we propose reinforcement learning (RL), which can consider various objectives to determine control inputs, as an ACC controller. To balance the performance of following a preceding vehicle and reducing jerk, RL rewards are designed using unique thresholds. Additionally, to balance performance and robustness to the zero-order delay (dead time) of the controlled system, dead time is also considered by scattering it randomly in the learning phase. As a result of this study, an RL agent trained using the proposed RL method and two LQR units specialized for followability and comfortability were simulated using Simulink® (MATLAB®). [ABSTRACT FROM AUTHOR]

A recent empirical study (Shi and Li, 2021) showed that commercial automated vehicles (AVs) became more unstable as the headway was set to a smaller value, implying possible intrinsic tradeoffs between safety, mobility, and stability aspects in AV following control design. This study aims to analytically explain the underlying vehicle control mechanism that dictates these tradeoffs. To this end, a robust optimization model is formulated based upon a parsimonious linear AV following model to capture the first-order tradeoffs between safety, mobility, and stability. The robust optimization model aims to maintain a sufficient safety buffer to avoid collisions against all possible realistic preceding vehicle trajectories. As opposed to a numerical solution, we managed to solve this model to an analytical solution that captures relationships between the key parameters determining safety, mobility, and stability. The analytical solution reveals that improving AV mobility (or reducing AV following headway) would require overcoming more safety challenges (e.g., enhancing vehicle control to maintain a short safety buffer) while causing more string-instability. The theoretical findings are consistent with the empirical observations in previous studies. Further, they provide a possible explanation for the observed string instability of commercial AV following control (e.g., adaptive cruise control) as a tradeoff for a smaller headway. Overall, this study lays a new methodology foundation for incorporating safety in traffic flow analysis that traditionally focused on only mobility and stability. Further, the findings yield a set of managerial insights into reasonable AV following design and its implications to emerging AV traffic management. • Build a robust optimization model relating safety, mobility, and stability. • Capture the tradeoffs between safety, mobility, and stability for commercial AVs. • Solve an analytical formulation for these tradeoffs. • Reveal that improving AV mobility would require overcoming more safety challenges while causing more string instability. • Show the consistency between theoretical findings and empirical observations. [ABSTRACT FROM AUTHOR]

In this article, we propose an ecological predictive cruise control method for connected and automated vehicles (CAVs) using data-driven predicted leading vehicle speed in a car-following scenario. Many existing studies assume that the leading vehicle's behavior is known when planning the trajectory of the ego vehicle. Unfortunately, predicting the future behavior of adjacent vehicles is extremely uncertain and inaccurate for use in control. To overcome this, we adopt the vector autoregressive model (VAR) that is well suited for generating simultaneous forecasts of the response variables when predicting the short-term behavior of the vehicle ahead. As many human drivers behave similarly in a car-following situation, vehicle connectivity is specifically used to drop hints of the behaviors of the cars following the connected car. Once the leading vehicle's future trajectory is predicted, the ego vehicle is controlled in a way that minimizes energy consumption by optimizing its speed trajectory while remaining safe. Through simulation case studies, we demonstrate that our approach can achieve improved energy efficiency considerably than the conventional strategy that cannot predict the speed of vehicles ahead. Given the recent market penetration of vehicle connectivity technologies and advanced driving assistance systems (ADAS), the proposed method is expected to have a high commercialization potential. [ABSTRACT FROM AUTHOR]

Advanced driver-assistance systems (ADAS) have matured over the past few decades with the dedication to enhance user experience and gain a wider market penetration. However, personalization features, as an approach to make the current technologies more acceptable and trustworthy for users, have been gaining momentum only very recently. In this work, we aim to learn personalized longitudinal driving behaviors via a Gaussian Process (GP) model. The proposed method learns from individual driver’s naturalistic car-following behavior, and outputs a desired acceleration profile that suits the driver’s preference. The learned model, together with a predictive safety filter that prevents rear-end collision, is used as a personalized adaptive cruise control (PACC) system. Numerical experiments show that GP-based PACC (GP-PACC) can almost exactly reproduce the driving styles of an intelligent driver model. Additionally, GP-PACC is further validated by human-in-the-loop experiments on the Unity game engine-based driving simulator. Trips driven by GP-PACC and two other baseline ACC algorithms with driver override rates are recorded and compared. Results show that on average, GP-PACC reduces the human override duration by 60% and 85% as compared to two widely-used ACC models, respectively, which shows the great potential of GP-PACC in improving driving comfort and overall user experience. [ABSTRACT FROM AUTHOR]

The ACC (adaptive cruise control) system has developed rapidly in recent years, and its reliability and safety have also attracted a lot of attention. The ACC system can realize automatic driving following the vehicle in the longitudinal range, and its reliability is closely related to the synchronization between two vehicles. Combined with formal modelling methods, this paper analyzes and detects the logical flaw that is poor synchronization in the following process of the ACC system from the perspective of synchronization. Aiming at avoiding this kind of logical flaw, this paper presents a novel optimized modelling solution based on the synchronization theory of Petri nets and further improves the calculation method of the synchronic distance. The simulation results reduce the token accumulation by an average of 91.357%, which demonstrates that the improved model can effectively improve reliability and reduce the risk of rear-end collision. [ABSTRACT FROM AUTHOR]

The traditional adaptive cruise system is responsible for delay in recognizing the cut-in/cut-out behaviour of front vehicle, and there is significant longitudinal acceleration of the vehicle fluctuation leading to reduced driver's comfort level and even dangerous situation. In this paper, the next generation simulation data set and back propagation (BP) neural network are used to train the vehicle lane change recognition model to recognize the lane change behaviour of the preceding vehicle. The higher controller adopts variable weight linear quadratic optimal control to adjust the weight parameters according to the recognition results of front vehicle to reduce the fluctuation of vehicle acceleration. The lower layer adopts fuzzy proportional-integral-derivative (PID) control to follow the expected acceleration and builds the vehicle inverse dynamic model. Through CarSim/Simulink co-simulation, the results show that, under the cut-in or cut-out and working conditions, the behaviour of the leading vehicle can be recognized, following target can be switched in advance, weight parameters can be adjusted and the large fluctuation of longitudinal acceleration can be reduced. [ABSTRACT FROM AUTHOR]

This paper analyzes trajectory observations from vehicles driving in platoon formation and they are equipped with Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems; aiming to quantify response delays. When a preceding vehicle induces a perturbation, the delay until the reaction of the following vehicle, often quoted as observable response time, can have negative implication to the traffic flow and other factors such as energy consumption, stability and safety. Quantifying such delays can help towards more realistic traffic simulation modeling. The analysis is performed based on empirical observations from three well-known experimental campaigns in the literature with data from ACC-driven and CACC-driven vehicle platoons. Three state-of-the-art techniques were implemented to provide quantitative results for the observed response times. The benefits and downsides of each technique are discussed as well. The results show that ACC systems do not exhibit a significant improvement compared to human drivers, yet, it can be concluded that the additional vehicle-to-vehicle communication incorporated in the CACC systems allows for a substantially higher traffic flow and possibly other benefits. [ABSTRACT FROM AUTHOR]

• To the best of our knowledge, this is the first publicly available study based on field operational test data that examined the use of Level 1 and 2 driving automation systems by road geometric characteristics on interstates and freeways. • Drivers were less likely to drive with the systems on as horizontal curves became sharper. • Even within the operational design domain, there are roadway features such as curves where drivers are less comfortable using the technology or that conditions may have been such that the systems disengaged. Little is known about how the actual use of Level 1 and 2 driving automation systems may be affected by geometric road characteristics in naturalistic driving environments. This study examined the use of these systems on horizontal curves on interstates and freeways. It used travel data collected in a field operational test conducted with two 2016 Land Rover Range Rover Evoque vehicles equipped with adaptive cruise control (ACC) and two 2017 Volvo S90 vehicles equipped with ACC and Pilot Assist (PA). Logistic regression models estimated changes in the likelihood of ACC use associated with horizontal curvature in the Evoque vehicles, and of PA and ACC use in the S90 vehicles, while accounting for traffic conditions. Drivers were less likely to drive with ACC or PA on as horizontal curves became sharper. In the Evoque vehicles, the likelihood of using ACC was 71.6% lower on the sharpest category of horizontal curves (those with a degree of curvature>2.5 degrees per 100 feet of arc or a radius smaller than 2,292 feet), compared with straight road segments or the flattest horizontal curve category (those with a degree of curvature <= 1.5 degrees per 100 feet of arc or a radius no <3,820 feet). In the S90 vehicles, the likelihood of using PA and ACC declined 74.6 and 66.3%, respectively, on the sharpest curves. Many driving automation systems face challenges on horizontal curves, even within their operational design domain. Future implementations that improve functionality may enhance driver experience and boost drivers' confidence in these systems, which should increase their use and maximize the safety benefits these systems might offer. [ABSTRACT FROM AUTHOR]

The ACC (adaptive cruise control) system is an important component of autonomous vehicle, and measuring inter-vehicle distance and velocity are basic environmental perception functions of it. Since camera-captured images provide richer environmental information at lower cost compared to sensors such as lidar, monocular vision-based solutions are gaining significant attention. However, traditional independent perception methods often overlook the importance of image context information in velocity estimation, and the detection errors of different monocular cameras at different distances may affect the accuracy of car-following. To address these issues, we propose a more effective monocular vision-based structure named TheNeoNet. TheNeoNet utilizes Graph Neural Network (GNN) to segment different pixel regions of the entire optical flow output feature map, thereby learning the relative relationships between different regions and endowing the motion feature extractor with stronger context awareness, significantly improving the accuracy of velocity estimation. After the vision-based perception, Soft Actor-Critic (SAC) method is employed to further optimize the car-following strategy. This method ensures that vehicles travel within a range of high monocular camera detection accuracy while balancing safety and comfort by minimizing predefined performance function indicators. The evaluation results show that compared to the state-of-the-art, the MSE of relative velocity estimation of TheNeoNet decreases by 3.83% and 83.50%, RMSE of distance estimation decreases by 14.49% and 21.39% on TuSimple dataset and KITTI dataset, respectively. The safety and comfort of proposed overall car-following strategy has increased compared with others, and it has good adaptability in various driving scenarios. • A car-following strategy based on monocular vision for the ACC system has been proposed. • TheNeoNet, an inter-vehicle distance and velocity measurement network based on monocular vision, has been proposed. • The car-following strategy considers the accuracy of visual detection at different distances. • TheNeoNet and car-following strategy have good adaptability in different driving scenarios. [ABSTRACT FROM AUTHOR]

Traditional Adaptive Cruise Control (ACC) systems often struggle to dynamically adapt to rapidly changing traffic conditions, resulting in suboptimal performance. Additionally, with fuel consumption emerging as a critical consideration alongside safety, there is a pressing need for more advanced solutions. This paper presents a novel approach to address these challenges by integrating Fuzzy ACC with Model Predictive Control, denoted as FACMPC. This integration aims to enhance both the longitudinal safety of AVs and fuel efficiency by considering real-time traffic conditions. The FACMPC system utilizes fuzzy logic inside the MPC, adaptively generates controller's weighting factors, allowing the system to adapt instantly to varying traffic environments and driving circumstances. The findings show that this adaptation improves the balance between driving safety, efficiency, and comfort. Additionally, three interruption scenarios, Alpha, Beta and Gama, are examined. In Alpha, the study evaluates the sensitivity of the FACMPC to disturbances by applying band-limited white noise to the lead vehicle velocity. In Beta, the AV experiences a loss of the lead vehicle velocity signal for a defined period, prompting safety considerations and assumptions. The Gama scenario includes a sensitivity analysis to account for variations and uncertainties in parameters by considering a range of ±5% around the nominal values for four key parameters: road slope, wind speed, wind direction, and rolling resistance. The findings indicate that the proposed controller's mean fuel consumption is 8.110, only a 3.21% increase over the nominal, compared to a 7.03% increase for the conventional ACC, demonstrating greater robustness against uncertainties. [ABSTRACT FROM AUTHOR]

• We confirm that ACC selects larger-than-human vehicle spacings in severe congestion. • Uncompacted ACC-queues are forecast to exacerbate urban congestion due to spillovers. • Means to compact ACC-queues are explored. Rush-period traffic conditions in two idealized settings are forecast into the future, when most drivers will presumably rely on adaptive cruise control (ACC) while operating their cars. Field experiments emulating the full range of congested conditions confirm that, for a given traffic speed, the spacings for ACC-vehicles tend to be larger than those in present-day congestion, where vehicles are fully human-controlled. These larger spacings mean smaller densities, which mean, in turn, that queues will be less compacted than at present. The queues will therefore expand over greater distances in the future, as more ACC-controlled vehicles enter the scene. These wider-spread, uncompacted queues spell trouble for cities, where queue storage during a rush is often a problem already. Simulations calibrated to the field-measured data were used to explore this unintended consequence of ACC for various foreseeable futures. Assumptions favorable to ACC were adopted throughout, to produce what are likely lower-bound estimates of future queue-storage problems. These lower bounds served as simple means to address forecast uncertainties. This is because our best-case outcomes for all futures examined are still far worse than the glowing predictions from elsewhere of how ACC may someday eliminate congestion. The first idealized setting was inspired by Downtown Los Angeles, where moderately high congestion already persists during each rush, but where physically long street links help with queue storage. We predict that, owing to ACC alone, rush-period vehicle hours traveled (VHT) on this first network will grow from present-day levels by as much as 12%. In the second setting, inspired by Midtown Manhattan where congestion is already severe and link lengths are short, VHT is predicted to grow by as much as 87%. Higher bottleneck capacities often promised of ACC are shown to be of little value when spillover queues constrain bottleneck flows from reaching those capacities. Adjusting onboard ACC controllers to produce smaller jam spacings was tested through simulation. The tests show how looming problems might be averted by this intervention, and futures thus improved. [ABSTRACT FROM AUTHOR]

The adaptive cruise control system relies heavily on the vehicle's longitudinal control algorithm. Traditional longitudinal control algorithms typically depend on precise vehicle dynamic modeling or complex controller parameter calibrations. In the research, an optimal hybrid strategy for the control of basic adaptive cruise control system is proposed. The proposed hybrid strategy is the combination of both the Model Predictive Control (MPC) and Coati Optimization Algorithm (COA) and hence it is named as MPC COA strategy. The fundamental goal of the proposed work is that a vehicle should maintain its current velocity when it approaches another vehicle that is driving at a steady velocity. The inputs for the proposed strategy are vehicle speed and acceleration. The inter-vehicle distance error, velocity error and acceleration are regulated based on the upper and lower limit of acceleration command with the help of proposed strategy. For accomplishing the required steadiness with the limitations in the given input and actual velocity with constant previous vehicle velocity, the proposed controller is utilized. The proposed controller is used to control the traction force of the host vehicle such that the vehicle speed follows the optimal speed trajectory as good as possible while ensuring constraints like distance to a preceding vehicle or speed limits. The performance of the proposed is implemented in the MATLAB platform and compared with existing approaches. The proposed technique demonstrates superior performance metrics, achieving the collision rate, rate of reaching maximum deceleration and ratio of exceeding comfortable deceleration or jerk are 1.27 %, 3.131 % and 9.052 %, respectively. [ABSTRACT FROM AUTHOR]

An adaptive cruise control system is highly used in conventional internal combustion engine vehicles, but due to the electrification of the vehicles, the adaptive cruise control system must be placed in relation with the electric motor control block. This paper presents the relationship between an adaptive cruise control system and the motor control in the case of an electric vehicle. An indirect filed-oriented control of an induction machine is considered for the electric traction system. The adaptive cruise control block computes the required acceleration to ensure the velocity set by the driver based on the set velocity, the actual velocity of the vehicle, a safe distance, and the relative distance between the host car and a leading car. The proposed system was implemented in MATLAB/Simulink and, also, in a low-scale laboratory experimental setup. The correct operation of the system was tested using a speed profile, and both systems, simulated and experimental, have the expected response. The velocity, acceleration, rotor speed, torque response, and relative and safe distance are the responses presented to prove the efficiency of the proposed implementation. [ABSTRACT FROM AUTHOR]