Reinforcement learning (RL) has gained significant interest for its potential to improve decision and control in autonomous driving. However, current approaches have yet to demonstrate sufficient scenario generality and observation generality, hindering their wider utilization. To address these limitations, we propose a unified benchmark simulator for RL algorithms (called IDSim) to facilitate decision and control for high-level autonomous driving, with emphasis on diverse scenarios and a unified observation interface. IDSim is composed of a scenario library and a simulation engine, and is designed with execution efficiency and determinism in mind. The scenario library covers common urban scenarios, with automated random generation of road structure and traffic flow, and the simulation engine operates on the generated scenarios with dynamic interaction support. We conduct four groups of benchmark experiments with five common RL algorithms and focus on challenging signalized intersection scenarios with varying conditions. The results showcase the reliability of the simulator and reveal its potential to improve the generality of RL algorithms. Our analysis suggests that multi-task learning and observation design are potential areas for further algorithm improvement.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Reinforcement Learning Benchmark for Autonomous Driving in General Urban Scenarios


    Contributors:
    Jiang, Yuxuan (author) / Zhan, Guojian (author) / Lan, Zhiqian (author) / Liu, Chang (author) / Cheng, Bo (author) / Li, Shengbo Eben (author)


    Publication date :

    2024-05-01


    Size :

    1804742 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Efficient-Enhanced Reinforcement Learning for Autonomous Driving in Urban Traffic Scenarios

    Yin, Jianwen / Jiang, Zhengmin / Liang, Qingyi et al. | IEEE | 2023


    Automated eco-driving in urban scenarios using deep reinforcement learning

    Wegener, Marius / Koch, Lucas / Eisenbarth, Markus et al. | Elsevier | 2021


    Urban Autonomous Driving of Emergency Vehicles with Reinforcement Learning

    Tong, Zherui / Ayala, Angel / Sandoval, Eduardo Benitez et al. | IEEE | 2023


    Autonomous Driving using Deep Reinforcement Learning in Urban Environment

    Hashim Shakil Ansari / Goutam R | BASE | 2019

    Free access