A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environments
A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environments
Haicheng Liao, Zhenning Li, Chengyue Wang, Bonan Wang, Hanlin Kong, Yanchen Guan, Guofa Li, Zhiyong Cui
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 5936-5944.
https://doi.org/10.24963/ijcai.2024/656
As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional approaches, our model excels in analyzing interactions and behavior patterns in mixed autonomy traffic scenarios. We introduce the Macao Connected Autonomous Driving (MoCAD) dataset as part of our contributions, which adds value to its complex urban driving scenarios. Our model represents a significant leap forward, achieving marked performance improvements on several key datasets. Specifically, it surpasses existing benchmarks with gains of 16.2% on the Next Generation Simulation (NGSIM), 27.4% on the Highway Drone (HighD), and 19.8% on the MoCAD dataset. Our proposed model shows exceptional proficiency in handling corner cases, essential for real-world applications. Moreover, its robustness is evident in scenarios with missing or limited data, outperforming most of the state-of-the-art baselines. This adaptability and resilience position our model as a viable tool for real-world autonomous driving systems, heralding a new standard in vehicle trajectory prediction for enhanced safety and efficiency.
Keywords:
Multidisciplinary Topics and Applications: MTA: Transportation
Agent-based and Multi-agent Systems: MAS: Human-agent interaction
Planning and Scheduling: PS: Applications
Robotics: ROB: Motion and path planning