We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving. Our representation is a spatio-temporal grid with each grid cell containing both the probability of the cell …
Humans learn to drive through both practice and theory, e.g. by studying the rules, while most self-driving systems are limited to the former. Being able to incorporate human knowledge of typical causal driving behaviour should benefit autonomous …
Domain generalization aims to learn a domain-invariant representation from multiple source domains so that a model can generalize well across unseen target domains. Such models are often trained with examples that are presented randomly from all …
Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency -- they should provide easy to interpret rationales for what triggers certain …