A Cognitive-Based Trajectory Prediction Approach for Autonomous Driving
IEEE Transactions on Intelligent Vehicles(2024)
摘要
In autonomous vehicle (AV) technology, the ability to accurately predict the
movements of surrounding vehicles is paramount for ensuring safety and
operational efficiency. Incorporating human decision-making insights enables
AVs to more effectively anticipate the potential actions of other vehicles,
significantly improving prediction accuracy and responsiveness in dynamic
environments. This paper introduces the Human-Like Trajectory Prediction (HLTP)
model, which adopts a teacher-student knowledge distillation framework inspired
by human cognitive processes. The HLTP model incorporates a sophisticated
teacher-student knowledge distillation framework. The "teacher" model, equipped
with an adaptive visual sector, mimics the visual processing of the human
brain, particularly the functions of the occipital and temporal lobes. The
"student" model focuses on real-time interaction and decision-making, drawing
parallels to prefrontal and parietal cortex functions. This approach allows for
dynamic adaptation to changing driving scenarios, capturing essential
perceptual cues for accurate prediction. Evaluated using the Macao Connected
and Autonomous Driving (MoCAD) dataset, along with the NGSIM and HighD
benchmarks, HLTP demonstrates superior performance compared to existing models,
particularly in challenging environments with incomplete data. The project page
is available at Github.
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关键词
Autonomous Driving,Trajectory Prediction,Cognitive Modeling,Knowledge Distillation,Interaction Understanding
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