Vehicle longitudinal control systems such as (commercially available) autonomous Adaptive Cruise Control (ACC) and its more sophisticated variant Cooperative ACC (CACC) could potentially have significant impacts on traffic flow. Accurate models of the dynamic responses of both of these systems are needed to produce realistic predictions of their effects on highway capacity and traffic flow dynamics. This paper describes the development of models of both ACC and CACC control systems that are based on real experimental data. To this end, four production vehicles were equipped with a commercial ACC system and a newly developed CACC controller. The Intelligent Driver Model (IDM) that has been widely used for ACC car-following modeling was also implemented on the production vehicles. These controllers were tested in different traffic situations in order to measure the actual responses of the vehicles. Test results indicate that: (1) the IDM controller when implemented in our experimental test vehicles does not perceptibly follow the speed changes of the preceding vehicle; (2) strings of consecutive ACC vehicles are unstable, amplifying the speed variations of preceding vehicles; and (3) strings of consecutive CACC vehicles overcome these limitations, providing smooth and stable car following responses. Simple but accurate models of the ACC and CACC vehicle following dynamics were derived from the actual measured responses of the vehicles and applied to simulations of some simple multi-vehicle car following scenarios.

https://hal.archives-ouvertes.fr/hal-01091160/document

Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2017.1291351

Policy and society related implications of automated driving: A review of literature and directions for future research

Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.

A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC)

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

A Survey of Deep Learning Applications to Autonomous Vehicle Control

A prototype of a longitudinal driving-assistance system, which is adaptive to driver behavior, is developed. Its functions include adaptive cruise control and forward collision warning/avoidance. The research data came from driver car-following tests in real traffic environments. Based on the data analysis, a driver model imitating the driver's operation is established to generate the desired throttle depression and braking pressure. Algorithms for collision warning and automatic braking activation are designed based on the driver's pedal deflection timing during approach (gap closing). A self-learning algorithm for driver characteristics is proposed based on the recursive least-square method with a forgetting factor. Using this algorithm, the parameters of the driver model can be identified from the data in the manual operation phase, and the identification result is applied during the automatic control phase in real time. A test bed with an electronic throttle and an electrohydraulic brake actuator is developed for system validation. The experimental results show that the self-learning algorithm is effective and that the system can, to some extent, adapt to individual characteristics.

An Adaptive Longitudinal Driving Assistance System Based on Driver Characteristics

Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance

A vehicle-to-vehicle distance control algorithm for stop-and-go cruise control has been proposed. The control algorithm consists of a distance control algorithm and throttle/brake control algorithm for acceleration tracking. A vehicle desired acceleration for vehicle-to-vehicle distance control has been designed using linear quadratic optimal control theory. Performance of the control algorithm has been investigated via computer simulations. It has been shown that the proposed control algorithm can provide satisfactory performance.

A vehicle-to-vehicle distance control algorithm for stop-and-go cruise control

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2008/data/papers/1741.pdf

Design, Tuning and Evaluation of Integrated ACC/CA Systems

This proposes a design methodology for a full-range ACC (Adaptive Cruise Control) with collision avoidance braking. The control scheme is designed to control the vehicle so that it would feel natural to the human driver and passengers during normal safe driving situations, to completely avoid collision in vehicle following situations and to reduce the severity of collisions in unexpected emergency situations. Driving situations are determined by using a non-dimensional warning index and time-to-collision (TTC). The proposed control scheme can provide natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent vehicle collision in dangerous traffic situations such as severe braking of the preceding vehicle during a vehicle following situation.

Ilki Moon

Papers

Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance

A vehicle-to-vehicle distance control algorithm for stop-and-go cruise control

Design, Tuning and Evaluation of Integrated ACC/CA Systems

Design of a Full-range ACC with Collision Avoidance Braking

Design of a full-range ACC with collision avoidacne/mitigation braking