Longitudinal Dynamic versus Kinematic Models for Car-Following Control Using Deep Reinforcement Learning

The majority of current studies on autonomous vehicle control via deep reinforcement learning (DRL) utilize point-mass kinematic models, neglecting vehicle dynamics which includes acceleration delay and acceleration command dynamics. The acceleration delay, which results from sensing and actuation delays, results in delayed execution of the control inputs. The acceleration command dynamics dictates that the actual vehicle acceleration does not rise up to the desired command acceleration instantaneously due to dynamics. In this work, we investigate the feasibility of applying DRL controllers trained using vehicle kinematic models to more realistic driving control with vehicle dynamics. We consider a particular longitudinal car-following control, i.e., Adaptive Cruise Control (ACC), problem solved via DRL using a point-mass kinematic model. When such a controller is applied to car following with vehicle dynamics, we observe significantly degraded car-following performance. Therefore, we redesign the DRL framework to accommodate the acceleration delay and acceleration command dynamics by adding the delayed control inputs and the actual vehicle acceleration to the reinforcement learning environment state, respectively. The training results show that the redesigned DRL controller results in near-optimal control performance of car following with vehicle dynamics considered when compared with dynamic programming solutions.Comment: Accepted to 2019 IEEE Intelligent Transportation Systems Conferenc

TRA-956: IMPROVING INTERSECTION THROUGHPUT USING CONNECTED VEHICLES

This paper proposes a connected vehicle based approach to improve the throughput at signalized intersections and ultimately increase the mobility of a transportation system. Connected vehicle technology demonstrates tremendous potential for improving safety and mobility, as it enables the real-time sharing of vehicle data, including position, speed, acceleration, etc., not only among vehicles but also between vehicles and infrastructure. The proposed approach takes advantage of such real-time data to develop a strategy that maximizes throughput of an isolated intersection locally. Accordingly, the problem is formulated as a two-step centralized optimization. There are two main processes in this method: optimization for vehicles in motion, and optimization for stopped vehicles. The first step maximizes the intersection throughput of vehicles in motion using advisory acceleration. The second one minimizes the total delay of the stopped vehicles by adjusting the positions at which vehicles stop. A case study is also presented to show the efficiency of the proposed approach, which improves the traffic flow throughput of an isolated signalized intersection and reduces the total delay of all vehicles

Evaluating Model Mismatch Impacting CACC Controllers in Mixed Traffic using a Driving Simulator

International audienceAt early market penetration, automated vehicles will share the road with legacy vehicles. For a safe transportation system, automated vehicle controllers therefore need to estimate the behavior of the legacy vehicles. However, mismatches between the estimated and real human behaviors can lead to inefficient control inputs, and even collisions in the worst case. In this paper, we propose a framework for evaluating the impact of model mismatch by interfacing a controller under test with a driving simulator. As a proof-of-concept, an algorithm based on Model Predictive Control (MPC) is evaluated in a braking scenario. We show how model mismatch between estimated and real human behavior can lead to a decrease in avoided collisions by almost 46%, and an increase in discomfort by almost 91%. Model mismatch is therefore non-negligible and the proposed framework is a unique method to evaluate them