SOURCE-LINKED INTELLIGENCE

READ: Learning Risk-Informed Fields for End-to-End Autonomous Driving

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Autonomous driving requires more than recognizing what is present in a scene: a planner must determine how road structure, surrounding agents, and their motion states should influence a future maneuver. Existing learning-based planners can capture these influences through latent scene features and trajectory decoders, but the relationship between environmental factors and candidate actions often remains implicit. This limits the ability to inspect, diagnose, or refine how scene context affects the safety of a predicted trajectory. Classical safety fields provide an explicit spatial representat

Read original source ↗ Open in workspace

recordType

paper

region

Global