Cooperative Adaptive Cruise Control: A Reinforcement Learning Approach

By Charles Desjardins, Brahim Chaib-draa. IEEE transactions on intelligent transportation systems (Print), 2011

330 citations recorded by the source corpus.

Recently, improvements in sensing, communicating, and computing technologies have led to the development of driver-assistance systems (DASs). Such systems aim at helping drivers by either providing a warning to reduce crashes or doing some of the control tasks to relieve a driver from repetitive and boring tasks. Thus, for example, adaptive cruise control (ACC) aims at relieving a driver from manually adjusting his/her speed to maintain a constant speed or a safe distance from the vehicle in front of him/her. Currently, ACC can be improved through vehicle-to-vehicle communication, where the current speed and acceleration of a vehicle can be transmitted to the following vehicles by intervehicle communication. This way, vehicle-to-vehicle communication with ACC can be combined in one single system called cooperative adaptive cruise control (CACC). This paper investigates CACC by proposing a novel approach for the design of autonomous vehicle controllers based on modern machine-learning techniques. More specifically, this paper shows how a reinforcement-learning approach can be used to develop controllers for the secure longitudinal following of a front vehicle. This approach uses function approximation techniques along with gradient-descent learning algorithms as a means of directly modifying a control policy to optimize its performance. The experimental results, through simulation, show that this design approach can result in efficient behavior for CACC.

Subfields: Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e81de96fcf20ff5520c20e4ee93db59bbf644cc9

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Citations in the corpus

Autonomous braking system via deep reinforcement learning (2017, methodology): “Recently, the DRL is applied to control systems for autonomous driving vehicle in [10], [11].”
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles (2020, background): “To reduce problem size, the authors of [75] used strategic-level decisions to set movement targets for the vehicles concerning the surrounding ones, and left the low-level control to classic solutions, which signiﬁcantly reduced the action space.”
A Survey of Deep Learning Applications to Autonomous Vehicle Control (2019, methodology): “For example, Desjardins & Chaib-Draa [23] used the time headway derivative to reward the agent for actions that helped it move towards the ideal time headway state.”
Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, background): “Motivated by this fact, cooperative adaptive cruise control [118], navigation coordination through intersections [119] through interactions, and cooperative lane changing”
Distributed Multiagent Coordinated Learning for Autonomous Driving in Highways Based on Dynamic Coordination Graphs (2020, methodology): “Later, Desjardins and Chaib-draa [9] investigated CACC by using function approximation RL techniques along with gradient-descent learning algorithms as a means of directly modifying a control policy for the secure longitudinal following of a front vehicle.”