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Learning Autonomous Control Policy for Intersection Navigation With Pedestrian Interaction
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AI
DOI：10.1109/TIV.2023.3256972.png)
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摘要
En 中文
In recent years, great efforts have been devoted to deep imitation learning for autonomous driving control, where raw sensory inputs are directly mapped to control actions. However, navigating through densely populated intersections remains a challenging task due to uncertainty caused by uncertain traffic participants. We focus on autonomous navigation at crowded intersections that require interaction with pedestrians. A multi-task conditional imitation learning framework is proposed to adapt both lateral and longitudinal control tasks for safe and efficient interaction. A new benchmark called IntersectNav is developed and human demonstrations are provided. Empirical results show that the proposed method can achieve a success rate gain of up to 30% compared to the state-of-the-art.
Keyword:
Task analysis
Autonomous vehicles
Navigation
Multitasking
Behavioral sciences
Electronics packaging
Computational modeling
Deep imitation learning
multi-task learning
autonomous driving control
interaction with pedestrians
期刊
I
IF:
14.3
论文数:
1.3K
被引数:
1.2W
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