The content discusses a novel approach to trajectory prediction for autonomous vehicles by incorporating human observation-inspired mechanisms. The model, named GaVa, outperforms existing baselines by significant margins across various datasets. By integrating insights from traffic behavior studies with advanced neural network architectures, the study demonstrates promising avenues for future research in autonomous driving.
The study introduces an interdisciplinary approach that combines principles of human cognition and observational behavior to enhance trajectory prediction models for autonomous vehicles. The proposed model, GaVa, incorporates adaptive visual sectors and dynamic traffic graphs to capture spatio-temporal dependencies among agents. Benchmark tests on multiple datasets show that GaVa outperforms state-of-the-art baselines significantly.
Through ablation studies, the importance of capturing social interactions and simulating drivers' changing visual focus with speed is validated. Removing components related to interaction-awareness or vision-awareness leads to decreased performance in trajectory prediction accuracy. The results emphasize the necessity of integrating traffic behavioral science into advanced neural network models.
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arxiv.org
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