SafeLight: A Reinforcement Learning Method toward Collision-Free Traffic Signal Control

Document Type

Conference Proceeding

Publication Date

6-27-2023

Abstract

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.

Identifier

85167985733 (Scopus)

ISBN

[9781577358800]

Publication Title

Proceedings of the 37th Aaai Conference on Artificial Intelligence Aaai 2023

External Full Text Location

https://doi.org/10.1609/aaai.v37i12.26729

First Page

14801

Last Page

14810

Volume

37

Grant

CNS–1948457

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS