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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]

A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction method. The significant properties of the proposed scheme are that: 1) it can accurately predict disturbances within the prediction horizon, and 2) the prediction results can be considered into the optimizing process to obtain a more efficient and accurate controller. As a result, the proposed adaptive cruise control (ACC) system can ensure driving safety and improve tracking accuracy and comfort performance while satisfying different driving styles. In detail, a large amount of naturalistic driving data is collected based on a real vehicle test platform at first. Then an adaptive optimization Gaussian process regression (AOGPR) is developed and trained with real measurements to predict the motion states of the preceding vehicle. The prediction module is embedded in SMPC to bind the collision conditions, tighten the states and finally construct a novel controller, i.e., AOGPR-SMPC controller. A bidirectional LSTM (BiLSTM) network is trained and tested for online recognizing driving styles to satisfy personalized car-following needs. The simulation and field tests verify and evaluate the proposed controller. The results demonstrate that the ACC system could realize personalized car-following according to the driver's driving style, and the proposed controller can obtain better tracking accuracy and comfort performance compared with the GPR-SMPC controller and MPC controller. [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]

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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]

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]

This study analyzes the problem of adaptive cruise control of vehicles in different driving cycles and divides diverse weight coefficient intervals for the vehicles under the different driving cycles to improve the adaptability of the vehicles in various environments. This paper first describes the driving environment of the adaptive cruise vehicle, and a model prediction algorithm with fixed weight coefficients is established to control the vehicle state. Then, a neural network is established to identify the vehicle driving cycles, the weight intervals are divided in accordance with different driving cycles, and the weight value is dynamically adjusted through fuzzy control. Lastly, the variable weight coefficients of different driving cycles are combined with the model prediction controller. The software cosimulation shows that the method designed in this paper plays a positive role in the fuel economy of adaptive cruise. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) has become an exigent domain of Intelligent Transport system (ITS). Providing efficient communication among rapidly moving vehicles is a challenging task in highway environment. This paper analysis the connectivity of High Speed Mobility and Lane Changing (HSMLC) based on Discretionary Lane Changing (DLC) approach for V2V environment. The Recurrent Neural Network System (RNN) which models a driver's decision to perform a Discretionary Lane Changing (DLC) process on highways. The RNN system model the DLC decision making using Adaptive Cruise Control (ACC) computational model. The ACC mechanism defines and extends traditional cursive control based on the input metrics which are extracted from Highway Traffic Management System database (HTMS). The RNN was trained and tested with HTMS data collected from Tamilnadu highway of India with ACC properties. The result part reviews the proposed DLC trajectories by lane changing phases, connectivity probability during DLC and packet delivery rate. During 65Kmph with 100 vehicles, the DLC takes highly 4.3 to 5.2 s for lane changing process and during this moment the PDR and throughput of the networks are 62 to 75% and 31.7 to 39Kbps, respectively. The simulation work done by two different simulators such as SUMO—mobility simulator and NS2—network simulator. [ABSTRACT FROM AUTHOR]

To improve the ride comfort and safety of a traditional adaptive cruise control (ACC) system when the preceding vehicle changes lanes, it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle. First, the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine, and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset. Second, according to the lane-changing intention and collision threat of the preceding vehicle, the target vehicle selection algorithm is studied under three different conditions: safe lane-changing, dangerous lane-changing, and lane-changing cancellation. Finally, the effectiveness of the proposed algorithm is verified in a co–simulation platform. The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver. In the case of a dangerous lane change, the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system; thus, it can effectively avoid collisions and improve the safety of the subject vehicle. [ABSTRACT FROM AUTHOR]

In order to be acceptable to drivers, the adaptive cruise control (ACC) systems need to be designed based on the analysis of human driver driving behavior. A new safe distance model is proposed in this paper based on the analysis of real-world driving test data. The goal of the control algorithm is to achieve naturalistic behavior of the vehicle that can comprehensively address the issue of driving safety, tracking performance, fuel economy and ride comfort. Firstly, a hierarchical control architecture is utilized which lower controller compensates for nonlinear vehicle dynamics and enables tracking of the desired acceleration. Then the acceleration rate of the following vehicle is introduced as state variables in the model of the car-following system for a more realistic and comprehensive description of dynamic evolution between the preceding car and the following car. Next, the control objectives above are formulated into a constrained quadratic programming problem under the framework of model predictive control (MPC). Finally, the simulation results show that the control strategy proposed in this paper can provide natural following performance that not only can satisfy driving safety, tracking performance but also can achieve fuel economy and ride comfort. [ABSTRACT FROM AUTHOR]

Self-driving safety is one of the major concerns raised with regard to pushing the use of automated vehicles on roads. Fully automated vehicles are forced to make appropriate decisions in an uncertain environment where driverless and human-driven vehicles share the road together. This study proposes a new model that can be used to enhance the autonomous driving behavior at a non-signalized intersection considering the traffic safety guarantee, delay time, and smooth driving. The proposed model is called the responsibility-sensitive safety-based partially observable Markov decision process model for the decision-making mechanism of automated vehicles. The model not only increases traffic safety guarantee and smooth driving, but also reduces the delay time. First, we generate some specific driving scenarios using the automated driving toolbox in MATLAB. Second, the driving strategy of automated vehicles is optimized by the partially observable Markov decision process framework using the adaptive cruise control system. Finally, the responsibility-sensitive safety algorithm is implemented under adaptive model predictive control. The proposed model performs better than the classical adaptive model predictive control. In the best case, the proposed model took a 31.60 % reduction in braking time and a 51.20 % improvement in smoothing speed control. [ABSTRACT FROM AUTHOR]

This paper describes the design of a longitudinal controller for an automated driving bus, which is expected to be a transportation method of the future. A linear longitudinal model is presented by approximating an aerodynamic drag force and rolling resistance force in a low speed range. Then, the feedback gains of a proportional integral (PI) controller for considering the longitudinal grade of the road are determined using a root locus method, with a constraint derived from the Chien–Hrones–Reswick method. A traffic light is predicted by the vehicle, so as to pass a signalized intersection smoothly. Further, a novel adaptive cruise control method is proposed for the automated driving bus, to reduce the acceleration of the vehicle. The performance of the controller is validated through pilot tests on public roads. [ABSTRACT FROM AUTHOR]

Road traffic crashes are currently one of the leading causes of death. Many accidents occur when the attention of a driver is diverted owing to fatigue or external factors. Adaptive cruise control (ACC) is a driver assistance system for safe vehicle transportation by combining a safe following distance with speed regulation. However, existing ACC systems fail to control the braking system when the preceding vehicle applies full brakes that can cause a jerk or collision. In addition, current ACC systems fail to consider road elevation and spatiotemporal problems that cause tracking errors. The existing issues result in high fuel consumption, discomfort during travel, and high error rates. To overcome these issues, a "Combinatorial Repeated Local Search and Simulated Annealing-Based Hill Climbing Optimization" (CRLSSA-HCO) technique is introduced for ACC with a predictive controller. In this technique, two models are introduced, namely the repeated local search-based hill climbing optimization (RLS-HCO) model, and the simulated annealing-based hill climbing optimization (SA-HCO) model. The RLS-HCO model performs local search optimization to avoid vehicle jerks and collisions by maintaining a safe distance between vehicles on a horizontal road. The RLS-HCO model operates in a spacing control mode and a speed control mode to maintain the safe distance. The input and control variables are optimized by performing a repeated local search, thereby reducing the fuel consumption and error rate. The SA-HCO model performs a global search optimization for avoiding traffic and addresses the spatiotemporal problems on hilly roads. The SA-HCO model optimizes the engine speed, engine torque, and gear ratio by using simulated annealing for reducing the fuel consumption and error rate. An experimental evaluation of the CRLSSA-HCO technique is carried out using performance metrics such as fuel consumption, distance error, and speed error, which are compared to those of state-of-the-art studies. [ABSTRACT FROM AUTHOR]

In this study we developed an autonomous braking algorithm to satisfy both safety and ride comfort of a vehicle. The proposed algorithm is composed of two-step braking strategies depending on the value of time-to-collision. The first step is a partial braking strategy to provide not only deceleration but also good ride comfort in a normal braking situation, and the second step is a full braking strategy to avoid forward collisions in an emergency braking situation. Further, the partial braking is divided into a recovery and a release zones. The former is to apply braking pressures for the safe deceleration, whereas the latter is to release the braking pressure smoothly for good ride comfort. To determine the partial braking pressures, the nonlinear characteristics of the tire friction is considered and the linear momentum of a preceding vehicle is calculated based on the extrapolation of its motion. Computer simulations using CarSim were executed combined with MATLAB/Simulink to implement the driving situations, and finally we obtained successful performances satisfying ride comfort as well as safety of the vehicle. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7 – 20.5 % higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2 – 17.3 %). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off. [ABSTRACT FROM AUTHOR]

The adaptive cruise control (ACC) problem can be transformed to an optimal tracking control problem for complex nonlinear systems. In this paper, a novel highly efficient model-free adaptive dynamic programming (ADP) approach with experience replay technology is proposed to design the ACC controller. Experience replay increases the data efficiency by recording the available driving data and repeatedly presenting them to the learning procedure of the acceleration controller in the ACC system. The learning framework that combines ADP with experience replay is described in detail. The distinguishing feature of the algorithm is that when estimating parameters of the critic network and the actor network with gradient rules, the gradients of historical data and current data are used to update parameters concurrently. It is proved with Lyapunov theory that the weight estimation errors of the actor network and the critic network are uniformly ultimately bounded under the novel weight update rules. The learning performance of the ACC controller implemented by this ADP algorithm is clearly demonstrated that experience replay can increase data efficiency significantly, and the approximate optimality and adaptability of the learned control policy are tested with typical driving scenarios. [ABSTRACT FROM AUTHOR]

This paper aims the development of adaptive cruise control (ACC) system, a technology of advanced driver assistance systems (ADAS), in an embedded application. This research has at its disposal a vehicle with a customized electronic control unit, with functions to support the study of ADAS. An ACC module was produced to allow the insertion of embedded controllers, communicating with the vehicle network and with a radar for future on-road applications. The dynamic model of the vehicle was estimated using the system identification theory, and a model validation was performed. The control system was divided in a cascade control loop, being the outer loop controller responsible for computing the cruise speed and the inner loop for tracking such speed. The outer loop controller was performed using a switching logic between cruise control and ACC modes. The inner loop controller was designed with the Dahlin control theory. The validation of the controllers was performed using a safe and controlled environment, with a dynamometer. [ABSTRACT FROM AUTHOR]

Existing adaptive cruise control systems adopt radars to track their preceding targets. The variation characteristics of the data collected by the radar when the preceding vehicle changes lanes and enters/exits a curve section are similar. As a result, the target identification may cause unsafe conditions on curve roads. For the proper classification of the curve entry/exit and lane change behaviors of the preceding target, speed, yaw rate, and steering angle were used to model road curvature real time. In this estimation algorithm, the slope of the preceding target’s motion trajectory was used to establish a status-identification and motion-tracking model for a straight road. A similar model for a curve road was constructed by using the lateral displacement variation characteristic as a distinction parameter. Field-test data were used to verify both models. Results showed that the classification accuracy rates of the model for the lane change and curve entry of the preceding vehicle when the host vehicle is on a straight road were 91.5 and 89.8%, respectively. When the host vehicle is traveling on a curve road, the classification accuracy rates for the lane change and curve exit of the preceding vehicle were 87.1 and 90.4%, respectively. The proposed algorithm could effectively enhance the safety performance of the active safety system. [ABSTRACT FROM AUTHOR]

In this paper, a novel spacing control law is developed for vehicles with adaptive cruise control (ACC) systems to perform spacing control mode. Rather than establishing a steady-state following distance behind a newly encountered vehicle to avoid collision, the proposed spacing control law based on model predictive control (MPC) further considers fuel economy and ride comfort. Firstly, a hierarchical control architecture is utilized in which a lower controller compensates for nonlinear longitudinal vehicle dynamics and enables to track the desired acceleration. The upper controller based on the proposed spacing control law is designed to compute the desired acceleration to maintain the control objectives. Moreover, the control objectives are then formulated into the model predictive control problem using acceleration and jerk limits as constrains. Furthermore, due to the complex driving conditions during in the transitional state, the traditional model predictive control algorithm with constant weight matrix cannot meet the requirement of improvement in the fuel economy and ride comfort. Therefore, a real-time weight tuning strategy is proposed to solve time-varying multi-objective control problems, where the weight of each objective can be adjusted with respect to different operating conditions. In addition, simulation results demonstrate that the ACC system with the proposed real-time weighted MPC (RW-MPC) can provide better performance than that using constant weight MPC (CW-MPC) in terms of fuel economy and ride comfort. [ABSTRACT FROM AUTHOR]

In this paper, the simulation validation of the hierarchical two-loop Adaptive Cruise Control (ACC) system is considered as a candidate for the application in the Advanced Driver Assistance Systems (ADAS), which aims to ensure driving safety and comfort as well as to improve fuel efficiency. Three different nonlinear model-based approaches for the inner-loop controllers are investigated for this system: the conventional Proportional-Integral Gain Scheduling controller (PI + GS) and two other strategies based on the simplified modelling of the vehicle dynamics: Balance-Based Adaptive Controller (B-BAC) and Nonlinear Model Predictive Controller (NMPC). The performance of each considered ACC system is tested by simulation under the same realistic scenarios for distance tracking mode and switching mode. The comparative criteria include driving safety, comfort of the driver and passengers and the fuel-economy aspect defined as BSFC (Brake Specific Fuel Consumption) index. The simulation results demonstrate that all the considered control algorithms meet the safety and car-following requirements while they provide slightly different level of driving comfort and fuel consumption, depending on the traffic situation and operating mode. [ABSTRACT FROM AUTHOR]

In a highly automated system, mode confusion is a significant human error factor that contributes to accidents. To suppress mode confusion in adaptive cruise control (ACC) systems of vehicles, we developed a new driver interface based on a formal approach to analyze and verify human-automation interaction. To enhance the driver's mode awareness, we developed a new ACC interface that eliminates inconsistent mode transitions by reconfiguring the modes. Then, a human-in-loop experiment was conducted in a simulated environment where a driving simulator was used to evaluate the state and mode awareness of drivers with the old and new interfaces. The experimental results showed that the proposed interface model, which was verified a formal method, was very effective in reducing mode confusion compared with the traditional interface model. [ABSTRACT FROM AUTHOR]

This study aims to develop car following models which simulate various adaptive cruise control (ACC) behaviors for a microscopic traffic simulator. There is a need for a microscopic traffic simulator to evaluate the impact of ACC penetration on highway traffic conditions. If the method of modeling of the simulator follows ACC technology as it is in the real world, the update frequency will be in millisecond order. This may result in an unexpected increase in the calculation time and often spoil the practical use of the simulator. To avoid this situation, it is necessary to develop a car following model which can simulate ACC with sufficient accuracy for impact assessment and which works in sub-second frequency, which is common for many microscopic traffic simulators. This paper outlines the Intelligent Driver Model and its derivations, which have many preferable features. Those model equations are modified to simulate three types of ACC behaviors which retain distance gap, time gap and time headway, and we verified their behaviors through computational platoon experiments with four cars. [ABSTRACT FROM AUTHOR]

The Cooperative Adaptive Cruise Control system (CACC) is an enhancement of the Adaptive Cruise Control system (ACC) that uses inter-vehicle communication to realize safe cruising at shorter inter-vehicle distance. To effectively communicate via broadcasting, a vehicle must identify the ID of the preceding vehicle, and extract that vehicle's information from the information sent by surrounding vehicles. In addition, the vehicle should ideally be able to reference information from other members of the platoon. In this manner, the system can evolve towards platooning with a very short inter-vehicle distance. On the other hand, if CACC is to acquire the same flexibility as ACC, the vehicle should handle common driving maneuvers such as lane-change of the preceding vehicle, cut-in of vehicles, and change of control mode of the preceding vehicle. Given the above requirements, this paper proposes control procedures and inter-vehicle communication schemes for implementing CACC of heavy-duty vehicles developed as part of the NEDO's Development of Energy-Saving ITS Technologies project. [ABSTRACT FROM AUTHOR]

A novel supervised Actor-Critic (SAC) approach for adaptive cruise control (ACC) problem is proposed in this paper. The key elements required by the SAC algorithm namely Actor and Critic, are approximated by feed-forward neural networks respectively. The output of Actor and the state are input to Critic to approximate the performance index function. A Lyapunov stability analysis approach has been presented to prove the uniformly ultimate bounded property of the estimation errors of the neural networks. Moreover, we use the supervisory controller to pre-train Actor to achieve a basic control policy, which can improve the training convergence and success rate. We apply this method to learn an approximate optimal control policy for the ACC problem. Experimental results in several driving scenarios demonstrate that the SAC algorithm performs well, so it is feasible and effective for the ACC problem. [ABSTRACT FROM AUTHOR]

A speed control algorithm for an ACC (Adaptive Cruise Control) system for curved roads is proposed based on driver behavior characteristics. As the foundation of this research, a driver speed model for curved roads is developed using a series of experimental data regarding driver behavior. To adapt the model to each driver's individual curve speed behavior, the coefficients of the model are identified in real time from the data sequences collected during drivers' manual operation stage by a self-learning algorithm based on a Recursive Least-Square (RLS) method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data collected in the manual operation phase, and the identification results are applied during the ACC automatic control phase. Based on the developed model, the ACC speed control algorithm is modified to provide each individual driver with a customized speed profile for the scenario of a curved road with no car ahead. Tests verify the applicability of the modified system. [ABSTRACT FROM AUTHOR]

Recently, the advanced driver assistance system (ADAS), which helps mitigate car accidents, has been developed using environmental detection sensors, such as long and short range radar, lidar, wide dynamic range cameras, ultrasonic sensors and laser scanners. Among these detection sensors, radars can quickly provide drivers with reliable information about the velocity, distance and direction of a target obstacle, as well as information about the vehicle in changing weather conditions. In the adaptive cruise control system (ACCS), three radar sensors are usually needed because two short range radars are used to detect objects in the adjacent lane and one long range radar is used to detect objects in-path. In this paper, low-cost radar based on a single sensor, which can detect objects in both the adjacent lane and in-path, is proposed for use in the ACCS. Before designing the proposed radar, we analyzed the world-wide radar technology and market trends for ACCS. Based on this analysis, we designed a novel radar sensor for the ACCS using radar components, such as an antenna, transceiver module, transceiver control module and signal processing algorithm. Finally, target detection experiments were conducted. In the experimental results, the proposed single radar can successfully complete the detection required for the ACCS. In the conclusion, the perspective and issues in the future development of the ACCS radar are described. [ABSTRACT FROM AUTHOR]

For vehicle adaptive cruise control (ACC) systems, the switching performance between throttle and brake determines the driving comfort, fuel consumption and service lives of vehicle mechanical components. In this paper, an ACC algorithm with the optimal switching control between throttle and brake is designed in model predictive control (MPC) framework. By introducing the binary integer variables, the dynamics of throttle and brake are integrated in one model expression for the controller design. Then the ACC algorithm is designed to satisfy not only safe car following, but also the optimal switching between throttle and brake, which leads to an online mixed integer quadratic programming solved by the nested two-loop method. The simulation results show that the proposed ACC algorithm meets the requirements of safe car following, outperforms the traditional algorithms by performing smoother responses, reducing the switching times between throttle and brake, and therefore improves driving comfort and fuel efficiency significantly. [ABSTRACT FROM AUTHOR]

Avoiding human overtrust in machines is a vital issue to establish a socially acceptable advanced driver assistance system (ADAS). However, research has not clarified the effective way of designing an ADAS to prevent driver overtrust in the system. It is necessary to develop a theoretical framework that is useful to understand how a human trust becomes excessive. This paper proposes a trust model by which overtrust can be clearly defined. It is shown that at least three types of overtrust are distinguished on the basis of the model. As an example, this paper discusses human overtrust in an adaptive cruise control (ACC) system. By conducting an experiment on a medium-fidelity driving simulator, we observed two types of overtrust among the three. The first one is that some drivers relied on the ACC system beyond its limit of decelerating capability. The second one is that a driver relied on the ACC systems by expecting that it could decelerate against a stopped vehicle. It is estimated through data analysis how those kinds of overtrust emerged. Furthermore, the possible ways for prevention of human overtrust in ADAS are discussed. [ABSTRACT FROM AUTHOR]

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles' movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle's movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy longitudinal control system with car-following speed ranging from 0 to 120 km/h, thereby achieving the main functions of both adaptive cruise control (ACC) and Stop&Go control. A fuzzy longitudinal controller is synthesized by inputting the difference of the actual relative distance and the safe distance obtained from the preceding vehicle, and the relative speed, and then outputting the pulse-width-modulation (PWM) signal to control the output forces of the vacuum boosters. With the use of the high-level controller from dSPACE, the fuzzy control law is easily and rapidly implemented using Matlab/Simulink for the experimental car, and the controller’s parameters can be changed and updated by analyzing data based on the relative distance using Lidar, the speed of the host vehicle, the opening of the throttle and the position of the braking pedal. For the sake of safe driving, experimental results are conducted by simulating the various possible car-following conditions for the ACC and Stop&Go controllers, thereby obtaining virtually relative distances and speeds to tune the controller’s parameters and ensure the safety of the controller. Several car following experiments are conducted to show that the proposed fuzzy longitudinal controller is capable of achieving the requirements of comfort and safety, and giving a satisfactory performance at high and low speed conditions. [ABSTRACT FROM AUTHOR]

This paper analyses driver trust and performance when using adaptive cruise control (ACC) in a situation involving a truck cutting into the lane in front of the ACC-equipped vehicle. The study was carried out using a mini driving simulator and a simulated ACC whose reference speed and time headway (THW) were preset to 130 km/h and 1.5 s, respectively. Questionnaires were used to analyse driver trust. Two kinds of drivers emerge from the analysis of driver behaviour during the cut-in situation: drivers who reclaimed control from the ACC before the device began to regulate the THW with regard to the truck, and drivers who braked after the device had begun its regulation. The latter demonstrated a higher level of trust in the ACC device itself while the former had a higher level of trust in the cooperation with the device. These findings are discussed in terms of over-reliance and well-calibrated trust. [ABSTRACT FROM AUTHOR]

Nowadays, with the advancement of computers, computer simulation linked with VR (Virtual Reality) technology has become a useful method for designing the automotive driving system. In this paper, the VR simulation system was developed to investigate the driving performances of the ASV (Advanced Safety Vehicle) equipped with an ACC (Adaptive Cruise Control) system. For this purpose, VR environment which generates visual and sound information of the vehicle, road, facilities, and terrain was organized for the realistic driving situation. Mathematical models of vehicle dynamic analysis, which includes the ACC algorithm, have been constructed for computer simulation. The ACC algorithm modulates the throttle and the brake functions of vehicles to regulate their speeds so that the vehicles can keep proper spacing. Also, the real-time simulation algorithm synchronizes vehicle dynamics simulation with VR rendering. With the developed VR simulation system, several scenarios are applied to evaluate the adaptive cruise controlled vehicle for various driving situations. [ABSTRACT FROM AUTHOR]

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior. [ABSTRACT FROM AUTHOR]

A vehicle cruise control strategy designed based on human drivers driving characteristics has been investigated. Human drivers driving patterns have been investigated using vehicle driving test data obtained from 125 participants. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would feel comfortable to the human driver. Vehicle following characteristics of the cruise controlled vehicle have been investigated using real-world vehicle driving test data and a validated simulation package. [ABSTRACT FROM AUTHOR]

Abstract. A system for hybrid adaptive cruise control (HACC) on high-speed roads designed as a combination of a radar-based ACC and visual perception is presented. The system is conceived to run on different performance levels depending on the actual perception capabilities. The advantages of a combination of the two different types of sensors are discussed in comparison to the shortcomings of each single sensor. A description of the visual lane detection and tracking procedure is given, followed by an overview of the vehicle detection, hypothesis generation, and tracking procedure. Enhanced robustness is achieved by cooperative estimation of egomotion and the dynamics of other vehicles using the lane-coordinate system as a common reference. Afterwards, the assignment of vehicles to lanes and the determination of the relevant vehicle for the longitudinal controller is described. [ABSTRACT FROM AUTHOR]

This paper reviews a collaborative research programme aimed at improving vehicle performance using adaptive control techniques. Initially the design of active suspension systems is considered, and the benefits of using a non-linear controller model with an adaptive control scheme are discussed. Adaptive schemes for active roll control are then considered, and the merits of incorporating a Smith predictor to accommodate for system delays are high-lighted. Preliminary research in adaptive cruise control and collision avoidance is discussed and plans for further developments are outlined. [ABSTRACT FROM AUTHOR]