DeepAI
Log In Sign Up

Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study

07/16/2020
by   Teng Liu, et al.
0

This work optimizes the highway decision making strategy of autonomous vehicles by using deep reinforcement learning (DRL). First, the highway driving environment is built, wherein the ego vehicle, surrounding vehicles, and road lanes are included. Then, the overtaking decision-making problem of the automated vehicle is formulated as an optimal control problem. Then relevant control actions, state variables, and optimization objectives are elaborated. Finally, the deep Q-network is applied to derive the intelligent driving policies for the ego vehicle. Simulation results reveal that the ego vehicle could safely and efficiently accomplish the driving task after learning and training.

READ FULL TEXT
07/16/2020

Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning

Autonomous driving is a promising technology to reduce traffic accidents...
07/16/2020

Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment

As a typical vehicle-cyber-physical-system (V-CPS), connected automated ...
07/16/2022

Robust AI Driving Strategy for Autonomous Vehicles

There has been significant progress in sensing, perception, and localiza...
02/06/2021

Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles

Testing and evaluation is a crucial step in the development and deployme...
12/21/2022

Decision-making and control with metasurface-based diffractive neural networks

The ultimate goal of artificial intelligence is to mimic the human brain...
07/16/2020

Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles

As a combination of various kinds of technologies, autonomous vehicles c...