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Advanced Driver Assistance Systems (ADAS) play a fundamental role in improving the driving experience by enhancing both comfort and safety. Currently, there is a lack of objective methods to quantify ADAS performance comprehensively, especially in different emergency conditions such as avoidable and inevitable collision states. This study introduces a framework for evaluating ADAS performance in any condition of potential interaction between two or more vehicles. For impacts that are avoidable by the system's intervention, assessments are based on comfort using vehicle acceleration (ISO 2631) and safety considering the minimum distance achieved between vehicles (clearance). In cases where the impact is inevitable, the performance indicator focuses on the injury risk for vehicle occupants associated with the collision and resulting from the ADAS intervention. To illustrate the application of this methodology, two case studies are presented, involving vehicles equipped and not equipped with ADAS. These cases respectively represent a real impact extracted from an in-depth accident database and a consumer program test for an Adaptive Cruise Control (ACC) function. By analyzing the behavior of autonomous steering and braking functions in a simulation environment as ADAS parameters are varied (i.e., the time between consecutive scans of the scenario by sensors and the time for full actuation of drive-by-wire systems), it becomes possible to compare performances of different types of intervention logic. This approach not only facilitates the identification of the performance of potentially available ADAS functions (as ACC) but also helps in highlighting the best logic applicable in a specific conflict. The retrieved information has the potential to indicate margins for improvement of ADAS already on the market, and simultaneously guide the development of next-generation ADAS with an increasing focus on autonomous driving and enhanced user technology acceptance. [ABSTRACT FROM AUTHOR]

This study aims to identify the factors that influence consumers' choice of autonomous cars in their new car purchase behavior. We surveyed stated preferences for autonomous driving technology and then analyzed the survey data using a multinominal logit model to examine how various factors—including price, consumer attributes, and respondents' evaluations of autonomous car characteristics—influence consumers' choice of car technology. The results of analysis indicate that experience with adaptive cruise control (ACC), as well as expectations about the enjoyment that autonomous cars will bring to consumers' daily lives, have a significant impact on their decision. [ABSTRACT FROM AUTHOR]

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]

Driverless Tractor into agriculture. The field of autonomous automation is of interest to researchers, and much has been accomplished in this area, of which this paper presents a detailed chronology. This paper can help one understand the trends in autonomous vehicle technology for the past, present, and future. We see a drastic change in autonomous vehicle technology since 1920s, when the first radio controlled vehicles were designed. In the subsequent decades, we see fairly autonomous electric cars powered by embedded circuits in the roads. By 1960s, autonomous cars having similar electronic guide systems came into picture. 1980s saw vision guided autonomous vehicles, which was a major milestone in technology and till date we use similar or modified forms of vision and radio guided technologies. Various semi-autonomous features introduced in modern cars such as lane keeping, automatic braking and adaptive cruise control are based on such systems. Extensive network guided systems in conjunction with vision guided features is the future of autonomous vehicles. It is predicted that most companies will launch fully autonomous vehicles by the advent of next decade. The future of autonomous vehicles is an ambitious era of safe and comfortable transportation. [ABSTRACT FROM AUTHOR]

Adaptive Cruise Control (ACC) is a technology that can reduce traffic. However, its availability in Iran is relatively limited compared to more developed countries. This research examines the acceptance and willingness to pay for ACC among Iranian drivers. Data from an online survey of 453 respondents were analyzed using an ordered logit model and a structural equation model. The results of modelings show that perceived ease of use and perceived usefulness affect attitudes towards using ACC, which in turn influence behavioural intentions. In addition, drivers who find ACC easy to use and user-friendly, own expensive vehicles, and female drivers who have experience with cruise control are more likely to pay for ACC. To enhance the adoption of ACC in Iran, it is recommended to target early adopters, especially women and capitalists, who can influence others with their positive feedback. The benefits of ACC for traffic safety and environmental sustainability should also be emphasized. Furthermore, future studies would better concentrate on public perceptions and assessing the necessary infrastructure for ACC in Iran.

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]

Adaptive Cruise Control (ACC) is a technology that can reduce traffic. However, its availability in Iran is relatively limited compared to more developed countries. This research examines the acceptance and willingness to pay for ACC among Iranian drivers. Data from an online survey of 453 respondents were analyzed using an ordered logit model and a structural equation model. The results of modelings show that perceived ease of use and perceived usefulness affect attitudes towards using ACC, which in turn influence behavioural intentions. In addition, drivers who find ACC easy to use and user-friendly, own expensive vehicles, and female drivers who have experience with cruise control are more likely to pay for ACC. To enhance the adoption of ACC in Iran, it is recommended to target early adopters, especially women and capitalists, who can influence others with their positive feedback. The benefits of ACC for traffic safety and environmental sustainability should also be emphasized. Furthermore, future studies would better concentrate on public perceptions and assessing the necessary infrastructure for ACC in Iran. • Developing a structural equation model and an orderd logit model using survey data. • Identifying factors influencing the acceptance of adaptive cruise control (ACC) technology. • Examining the variables that affect the willingness to pay for ACC. • Providing policies for the use of ACC to reduce environmental impacts. [ABSTRACT FROM AUTHOR]

Traffic congestion is becoming more prevalent as the number of vehicles on the roads continues to rise. To shorten travel times and enhance driver comfort, a range of Advanced Driver Assistance Systems (ADAS) has been developed to assist drivers in urban areas and on highways. The demand for increasing road capacity has introduced the concept of vehicle platooning, where the use of the Adaptive Cruise Control (ACC) system is a key function within the advanced driver assistance (ADAS) technology, this technology manages the vehicle's longitudinal control in specific driving conditions. Cars equipped with ACC can efficiently maintain a set distance from the vehicle ahead, easing the driver's workload while offering advantages such as improved road capacity, lower fuel consumption, and reduced pollution emissions. However, they can be susceptible to string instability, resulting in the amplification of oscillations caused by speed variations along the platoon's rear. This paper presents a string stability analysis of car platoons equipped with ACC system based on a heuristic method by choosing of the constant time headway policy (CTHP). The constant time headway policy selection for string stability is based on the Nyquist diagram of the transfer function of the spacing errors between two cars. A platoon operated by using distance-based ACC control structures is implemented. These structures employ a linear quadratic regulator (LQR) using a dual integrator. The simula-tion results were acquired by modeling and simulating the studied platoon within Matlab/Simulink. [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]

The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving (JAD) strategies on circular and linear scenarios have been evaluated. The manual vehicle is the intelligent driver model (IDM) and human driver model (HDM), respectively. The results suggest that on the ring road, the traffic performance of mixed traffic improves gradually with the increase of the proportion of CAVs under the ACC. Moreover, the traffic performance for the JAD strategy does not improve infinitely with the increase in the number of CAVs. Conversely, the FS strategy suppresses traffic waves at the cost of reducing traffic flow, and more CAVs are not beneficial for mixed traffic. It is interesting to note that under optimal performance in these three strategies, the FS strategy has the lowest number of CAVs, while the ACC strategy has the highest number of CAVs. For the linear road, it demonstrates that the JAD strategy exhibits a superior performance compared to the ACC. However, the FS strategy cannot dissipate traffic waves due to an insufficient buffer gap. Different models have varying effects on different strategies. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) assists automobiles in preserving a safe following distance and adhering to speed limits. This advanced driver-assistance system (ADAS) modifies the car's speed to keep a safe gap from oncoming traffic. All vehicle types include combustion engines, pure electric vehicles, hybrid electric vehicles, and methods of operation; controllers are designed to react to cruise control signals and provide an efficient route profile according to the surrounding environment and instantaneous vehicle performance characteristics. ACC uses a perception system to measure the forward vehicle's current distance, speed, and acceleration relative to the host vehicle. Some of these systems use lasers, radar, cameras, or a combination of these sensors to determine the distance and speed of the leading vehicle. Other systems even use wireless communication to collect data from surrounding vehicles. ACC can help reduce stress on long drives, increase road safety, prevent accidents, and enhance traffic flow energy efficiency. This paper aims to introduce a comprehensive study of the research on ACC and mention different controlling techniques used to deal with the problem. Furthermore, a discussion of each method with its cons and pros is mentioned too. First, an introduction to the ACC system and control approaches with a brief discussion of their main principle is presented. Next, various application cases of ACC are presented. These applications include lateral dynamics, wireless technology, energy vehicles, navigation data, and practical experimental tests. At last, future guidance and challenges are discussed.

This paper presents the ACC system for autonomous trams, including three core processes: object detection, collision assessment and control. The system utilizes the YOLOX deep learning model for camera-based object detection and a clustering approach for LiDAR point clouds, integrating both outputs through a late fusion method for enhanced accuracy. The positions of objects and the tram are projected onto a data map to assess collision risk. This data map, which is a virtual 2D space configured with the track’s UTM coordinates and ROI lines, is used to determine whether objects are located within the ROI. The ACC system adjusts the tram’s speed to control acceleration or deceleration based on the relative distances of objects within the ROI. Experiments on a test track demonstrate effective collision avoidance and reliable ACC system operation under predefined scenarios.

A substantial number of vehicles nowadays are equipped with adaptive cruise control (ACC), which adjusts the vehicle speed automatically. However, experiments have found that commercial ACC systems which only detect the direct leader amplify the propagating disturbances in the platoon. This can cause severe traffic congestion when the number of ACC-equipped vehicles increases. Therefore, an ACC system which also considers the second leader further downstream is required. Such a system enables the vehicle to achieve multi-anticipation and hence ensure better platoon stability. Nevertheless, measurements collected from the second leader may be comparatively inaccurate given the limitations of current state-of-the-art sensor technology. This study adopts deep reinforcement learning to develop ACC controllers that besides the input from the first leader exploits the additional information obtained from the second leader, albeit noisy. The simulation experiment demonstrates that even under the influence of noisy measurements, the multi-leader ACC platoon shows smaller disturbance and jerk amplitudes than the one-leader ACC platoon, indicating improved string stability and ride comfort. Practical takeaways are twofold: first, the proposed method can be used to further develop multi-leader ACC systems. Second, even noisy data from the second leader can help stabilize traffic, which makes such systems viable in practice.

Workload and distraction are problems that affect road driving and can cause very serious accidents. Drive assistance systems are very important to help drivers relieve fatigue of driving by increasing road safety and reducing the number of accidents. The Adaptive Cruise Control (ACC) is a useful driver assistance system to set and maintain a safe distance from the front vehicle, modulating speed and limiting sudden braking. However, the effects of the ACC on overall driver behaviour have not yet been thoroughly investigated. In this study, the authors assessed the influence of ACC on mental workload and attention of drivers in a real driving environment. The results of several evaluations were considered: subjective assessment of workload (NASA-TLX), physiological measurements of workload (brain activity through electroencephalographic technique and the analysis of visual behaviour through an eye monitoring device) and performance-based measurements (via Vbox Pro Video mounted on the vehicle). 52 drivers were involved in the study, 26 ACC experts and 26 non-experts, who drove along the ring road of Bologna (Tangenziale di Bologna) (Italy), both using ACC and manually, without the help of any device. During the test a secondary task was introduced: the sudden arrival of a braking car. Results showed that the use of ACC increased distraction when driving.

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.

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.

This article delves into the intricate world of controlling the longitudinal dynamics of autonomous vehicles. In the first part, we studied two distinct controllers: the Super Twisting Sliding Mode Control and its modified version enriched with the integration of fuzzy logic, applied to the longitudinal dynamics of the autonomous automobile to follow a desired speed longitudinal profile, the two controllers are compared with a Neural Network-Based Non-singular Terminal Sliding-Mode Control, the system takes the throttle and brake as its inputs and delivers speed and acceleration as outputs. The overarching objective is to ensure that the controlled vehicle maintains a close and precise alignment with the desired speed profile. The second part of our research is dedicated to the development of adaptive cruise control systems and cruise control according to the safety conditions. This controller consists of two blocks low and upper controller, in upper controller the inputs are the speeds of the automobile in front and of the autonomous automobile itself, the safety distance, the measured distance. The output is the desired acceleration. The objective is to maintain a distance between the front vehicles, greater than or equal to the safety distance. For this, to achieve this task, we have implemented a control system known as the Proportional Integral Derivative (PID) controller in the adaptive cruise control system to control this system. In the low controller block, the same controllers used in the first part: the Super Twisting Sliding Mode Control and its modified version based on fuzzy logic, are applied, the system inputs are throttle and brake, and the outputs are speed and acceleration. This system is processed by MATLAB code, we obtained a better result with our proposed controller such that the maximum absolute speed error is equal to 0.0144 m/s in the first case of speed tracking, and to 0.006 m/s in the second case of the using adaptive cruise control, the illustrations below show the efficiency and robustness of these controllers.

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]

In this paper, a delay compensation feedback controller based on reinforcement learning is proposed to adjust the time interval of the adaptive cruise control (ACC) vehicle agents in the traffic congestion by introducing the proximal policy optimization (PPO) scheme. The high‐speed traffic flow is characterized by a two‐by‐two Aw Rasle Zhang nonlinear first‐order partial differential equations (PDEs). Unlike the backstepping delay compensation control,23 the PPO controller proposed in this paper consists of the current traffic flow velocity, the current traffic flow density and the previous one step control input. Since the system dynamics of the traffic flow are difficult to be expressed mathematically, the control gains of the three feedback can be determined via learning from the interaction between the PPO and the digital simulator of the traffic system. The performance of Lyapunov control, backstepping control and PPO control are compared with numerical simulation. The results demonstrate that PPO control is superior to Lyapunov control in terms of the convergence rate and control efforts for the traffic system without delay. As for the traffic system with unstable input delay value, the performance of PPO controller is also equivalent to that of backstepping controller. Besides, PPO is more robust than backstepping controller when the parameter is sensitive to Gaussian noise. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent speed adaption system for vehicles on conventional roads. The fuzzy logic based expert system outputs a recommended speed to ensure both safety and passenger comfort. This intelligent system includes geometrical features of the road, as well as subjective perceptions of the drivers. It has been developed and checked with real data that were measured with an instrumental system incorporated in a vehicle, on several two‐lane roads located in the Madrid Region, Spain. Along with the road geometrical characteristics, other input variables to the system are external factors, such as weather conditions, distance to the preceding vehicle, tire pressure, and other subjective criteria, such as the desired comfort level, selected by the driver. The expert system output is the most suitable speed for the specific road type, considering real factors that may modify the category of the road and thus, the appropriate speed. This information could be added to the adaptive cruise control of the vehicle. The recommended speed can be a very useful input for both, drivers and the autonomous vehicles, to improve safety on the road system. [ABSTRACT FROM AUTHOR]