Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights

• URL: http://arxiv.org/abs/2502.20084v1
• Date: Thu, 27 Feb 2025 13:43:17 GMT
• Title: Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights
• Authors: Haicheng Liao, Chengyue Wang, Kaiqun Zhu, Yilong Ren, Bolin Gao, Shengbo Eben Li, Chengzhong Xu, Zhenning Li,
• Abstract summary: In driving scenarios, a vehicle's trajectory is determined by the decision-making process of human drivers.<n>Previous models fail to capture the true intentions of human drivers, leading to suboptimal performance in long-term trajectory prediction.<n>We introduce a Cognitive-Informed Transformer (CITF) that incorporates a cognitive concept, Perceived Safety, to interpret drivers' decision-making mechanisms.
• Score: 18.92479778025183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In mixed autonomous driving environments, accurately predicting the future trajectories of surrounding vehicles is crucial for the safe operation of autonomous vehicles (AVs). In driving scenarios, a vehicle's trajectory is determined by the decision-making process of human drivers. However, existing models primarily focus on the inherent statistical patterns in the data, often neglecting the critical aspect of understanding the decision-making processes of human drivers. This oversight results in models that fail to capture the true intentions of human drivers, leading to suboptimal performance in long-term trajectory prediction. To address this limitation, we introduce a Cognitive-Informed Transformer (CITF) that incorporates a cognitive concept, Perceived Safety, to interpret drivers' decision-making mechanisms. Perceived Safety encapsulates the varying risk tolerances across drivers with different driving behaviors. Specifically, we develop a Perceived Safety-aware Module that includes a Quantitative Safety Assessment for measuring the subject risk levels within scenarios, and Driver Behavior Profiling for characterizing driver behaviors. Furthermore, we present a novel module, Leanformer, designed to capture social interactions among vehicles. CITF demonstrates significant performance improvements on three well-established datasets. In terms of long-term prediction, it surpasses existing benchmarks by 12.0% on the NGSIM, 28.2% on the HighD, and 20.8% on the MoCAD dataset. Additionally, its robustness in scenarios with limited or missing data is evident, surpassing most state-of-the-art (SOTA) baselines, and paving the way for real-world applications.

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