Motion (physics)

Cruise control

Quantum mechanics

Robot

Control (management)

Vehicle dynamics

Engineering

Biology

Motion planning

Control engineering

Computer science

Mathematical optimization

Controller (irrigation)

Physics

Reinforcement learning

Simulation

Mathematics

Agronomy

Automotive engineering

Artificial intelligence

Optimal control

Action (physics)

IEEE Transactions on Vehicular Technology

In this study, a decision-making and motion planning controller with continuous action space is constructed in the highway driving scenario based on deep reinforcement learning. In the decision-making and planning problem, the goal is to achieve the safety, efficiency, and comfort of automated vehicles. In the driving scenario, the surrounding vehicles are controlled by the intelligent driver model and a general model (minimizing overall braking induced by lane change, MOBIL), which enables them to react to the environment and mimic the vehicle interactions on the highway. Given the uncertainties in the driving conditions, a specific deep reinforcement learning technique, called soft actor-critic, is used to solve the decision-making and planning problem with continuous action space. Simulation results show that the proposed method can solve the decision-making and motion planning problem in the interactive traffic environment to carry out safe lane-change maneuvers and cruise at high speed. In addition, two control policies are developed with different weights on safety, efficiency, and comfort.

Highway Decision-Making and Motion Planning for Autonomous Driving via Soft Actor-Critic