Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction

TL;DR

This paper introduces HLTP++, a brain-inspired trajectory prediction model for autonomous driving that uses knowledge distillation and a novel neural network to improve accuracy and efficiency, especially in complex or incomplete data scenarios.

Contribution

The paper presents a new human-like trajectory prediction framework with a teacher-student model and a Fourier adaptive spike neural network, enhancing accuracy and efficiency over existing methods.

Findings

Reduces trajectory prediction error by over 11% on NGSIM dataset.

Achieves 25% error reduction on HighD dataset.

Demonstrates robustness in environments with incomplete data.

Abstract

Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to improve trajectory prediction in AD. HLTP++ incorporates a novel teacher-student knowledge distillation framework. The "teacher" model equipped with an adaptive visual sector, mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. On the other hand, the "student" model focuses on real-time interaction and human decision-making, drawing parallels to the human memory storage mechanism. Furthermore, we improve the model's efficiency by introducing a new Fourier Adaptive Spike Neural Network (FA-SNN), allowing for faster and more precise predictions with fewer parameters. Evaluated using the NGSIM, HighD, and MoCAD benchmarks, HLTP++ demonstrates superior performance compared to existing models, which reduces the predicted trajectory error with over 11% on the NGSIM dataset and 25% on the HighD datasets. Moreover, HLTP++ demonstrates strong adaptability in challenging environments with incomplete input data. This marks a significant stride in the journey towards fully AD systems.