3 papers
cs.RO2024
Learning to Drive via Asymmetric Self-Play
Chris Zhang, Sourav Biswas, Kelvin Wong +5
Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of dr…
cs.CV2024
DeTra: A Unified Model for Object Detection and Trajectory Forecasting
Sergio Casas, Ben Agro, Jiageng Mao +4
The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving. These tasks are typically executed in a cascading ma…
cs.CV2024
UnO: Unsupervised Occupancy Fields for Perception and Forecasting
Ben Agro, Quinlan Sykora, Sergio Casas +2
Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world -- t…