This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective. 

Distributed Adaptive Consensus Protocol for Connected Vehicle Platoon With Heterogeneous Time-Varying Delays and Switching Topologies

Autonomous vehicles in a platoon determine the control inputs based on the system state information collected and shared by the Internet of Things (IoT) devices. Deep reinforcement learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn an efficient car-following policy with convergence stability when there are multiple following vehicles in a platoon, especially with unpredictable leading vehicle behavior. In this context, we adopt an integrated DRL and dynamic programming (DP) approach to learn autonomous platoon control policies, which embeds the deep deterministic policy gradient (DDPG) algorithm into a finite-horizon value iteration framework. Although the DP framework can improve the stability and performance of DDPG, it has the limitations of lower sampling and training efficiency. In this article, we propose an algorithm, namely, finite-horizon-DDPG with sweeping through reduced state space using stationary approximation (FH-DDPG-SS), which uses three key ideas to overcome the above limitations, i.e., transferring network weights backward in time, stationary policy approximation for earlier time steps, and sweeping through reduced state space. In order to verify the effectiveness of FH-DDPG-SS, simulation using real driving data is performed, where the performance of FH-DDPG-SS is compared with those of the benchmark algorithms. Finally, platoon safety and string stability for FH-DDPG-SS are demonstrated. 

/pdf/autonomous-platoon-control-with-integrated-deep-2qzx3gvx.pdf

Autonomous Platoon Control With Integrated Deep Reinforcement Learning and Dynamic Programming

Autonomous vehicles in a platoon determine the control inputs based on the system state information collected and shared by the Internet of Things (IoT) devices. Deep reinforcement learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn an efficient car-following policy with convergence stability when there are multiple following vehicles in a platoon, especially with unpredictable leading vehicle behavior. In this context, we adopt an integrated DRL and dynamic programming (DP) approach to learn autonomous platoon control policies, which embeds the deep deterministic policy gradient (DDPG) algorithm into a finite-horizon value iteration framework. Although the DP framework can improve the stability and performance of DDPG, it has the limitations of lower sampling and training efficiency. In this article, we propose an algorithm, namely, finite-horizon-DDPG with sweeping through reduced state space using stationary approximation (FH-DDPG-SS), which uses three key ideas to overcome the above limitations, i.e., transferring network weights backward in time, stationary policy approximation for earlier time steps, and sweeping through reduced state space. In order to verify the effectiveness of FH-DDPG-SS, simulation using real driving data is performed, where the performance of FH-DDPG-SS is compared with those of the benchmark algorithms. Finally, platoon safety and string stability for FH-DDPG-SS are demonstrated.

Hierarchical lane-changing control for vehicle platoons in prescribed performance

A Cooperative Lane Change Control Strategy for Cooperative Adaptive Cruise Control Platoons with Insufficient Headway Gaps

A multimode hybrid automaton is proposed for setting vehicle platoon modes with velocity, distance, length, lane position, and other state information. Based on a vehicle platoon shift movement under different modes, decisions are made based on key conditional actions, such as sudden acceleration changes because of vehicle distance changes, emergency braking to avoid collisions and free-lane changing choices adapted to various traffic conditions, so as to ensure effortless movement and safety in the multimode shift. With a 3-degree (longitudinal, lateral, and yaw directions) of the freedom coupled model, a hybrid vehicle platoon controller is proposed using nonsingular terminal sliding-mode control to ensure fast and steady tracking on the hybrid automaton outputs during the multimode shift process. The convergence of the hybrid controller in finite time is also analyzed with the Lyapunov exponential stability. The analysis result proves that the proposed controller not only ensures the stability of the individual vehicle and the vehicle platoon but also ensures the stability of the multimode shift movement system. The proposed cooperative driving strategy for vehicle platoon is evaluated using simulations, where varying traffic conditions and the influence of cutting off are considered in conjunction with demonstration simulations of a vehicle platoon’s cruising, following, lane changing, overtaking, and moving in/out of garage functions.

A Novel Multimode Hybrid Control Method for Cooperative Driving of an Automated Vehicle Platoon

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A Novel Multimode Hybrid Control Method for Cooperative Driving of an Automated Vehicle Platoon