Jekl, Bence and Dabčević, Zvonimir and Németh, Balázs and Škugor, Branimir and Gáspár, Péter (2025) Scenario-Optimization-Based Velocity Planning of Autonomous Vehicles for Interacting With Pedestrians. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 26 (4). pp. 5382-5395. ISSN 1524-9050 10.1109/TITS.2025.3531506

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Abstract

This paper presents a velocity planning method for autonomous vehicles (AVs) to guarantee safe interactions with pedestrians at unsignalized crosswalks and with surrounding vehicles on the AV's route. The method is structured within a hierarchical framework that includes robust control, a learning-based component, and a supervisory element. The learning-based component is trained using reinforcement learning techniques to reduce traveling time, minimize control interventions, and set the priority ratio between the AV and pedestrians. The supervisory element employs scenario optimization, using statistical data on pedestrian motions to ensure collision avoidance. A complex game-theory-based pedestrian model is formulated and analyzed in order to evaluate the effectiveness of the proposed velocity planning method. Extensive simulations are performed using the high-precision traffic simulator software SUMO. These simulations evaluate various aspects of the velocity planner, including computation time, traveling time, control interventions, and parameter settings. The results demonstrate the method's ability to achieve real-time implementation while maintaining safety and performance objectives. © 2025 IEEE.

Item Type:	Article
Uncontrolled Keywords:	motion planning; reinforcement learning; Robust control; planning method; Autonomous Vehicles; Scenario approach; Data-driven control; Pedestrian safety; Pedestrian modeling; Pedestrian models; Reinforcement learning techniques; Traveling time; Velocity planning; Autonomous pedestrians; Scenario optimizations;
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Systems and Control Lab
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	01 Apr 2025 11:05
Last Modified:	01 Apr 2025 11:05
URI:	https://eprints.sztaki.hu/id/eprint/10878

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