Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization
End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on a vehicle, creating a fundamental mismatch between training and execution. Beyond the commonly studied effects of covariate shift and causal confusion, we identify a complementary factor for this open-loop/closed-loop gap: waypoint-based supervision and displacement metrics do not ensure that the intermediate trajectory is physically coherent or easy for the controller to track. We observe that these inconsistencies concentrate primarily at intermediate waypoints, while the predicted endpoint remains comparatively reliable. Based on this obs...
| Authors | Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski et al. |
|---|---|
| Primary category | cs.RO |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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