Similar to human drivers, it is crucial for Autonomous Driving (AD) systems to comprehend and adhere to traffic rules effectively. This adherence ensures safety and societal acceptance of Autonomous Vehicles (AVs). Integrating traffic rules into the learning process and real-time monitoring of rule adherence are therefore essential for the successful implementation and widespread adoption of AD technologies. To address the need for traffic rule conformance analysis, we introduce a framework based on BARK, our in-house developed scenario simulator. Within this framework, we formalize rules using Linear Temporal Logic (LTL) formulas and employ the quantitative semantics of Signal Temporal Logic (STL) to assess rule compliance with finer granularity. These rules are then integrated into the learning process of Deep Reinforcement Learning (DRL) agents. By conducting experiments across various simulated scenarios within the framework, as well as on a public dataset, we underscore the importance of integrating both traffic rules and quantitative semantics of STL into the development of behavior planner agents. This integration enhances their efficacy and safety in real-world applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Rule Integration with Temporal Logic in Deep Reinforcement Learning for Behavior Planning


    Contributors:


    Publication date :

    2024-09-24


    Size :

    1009776 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Deep Reinforcement Learning for Optimizing Route Planning in Urban Traffic

    Mittal, Mudit / Sehgal, Archana / Varshney, Neeraj et al. | IEEE | 2025



    Temporal Logic Guided Safe Model-Based Reinforcement Learning

    Cohen, Max / Belta, Calin | Springer Verlag | 2023