2020/12/24 by Ershad Banijamali, Banijamali, Ershad, Mohsen Rohani +7
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Online Learning and Analytics #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2012.13478
openalex publication_date 2020/12/24 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
In autonomous driving (AD), accurately predicting changes in the environment\ncan effectively improve safety and comfort. Due to complex interactions among\ntraffic participants, however, it is very hard to achieve accurate prediction\nfor a long horizon. To address this challenge, we propose prediction by\nanticipation, which views interaction in terms of a latent probabilistic\ngenerative process wherein some vehicles move partly in response to the\nanticipated motion of other vehicles. Under this view, consecutive data frames\ncan be factorized into sequential samples from an action-conditional\ndistribution that effectively generalizes to a wider range of actions and\ndriving situations. Our proposed prediction model, variational Bayesian in\nnature, is trained to maximize the evidence lower bound (ELBO) of the\nlog-likelihood of this conditional distribution. Evaluations of our approach\nwith prominent AD datasets NGSIM I-80 and Argoverse show significant\nimprovement over current state-of-the-art in both accuracy and generalization.\n