RiSE-Drive: Risk-Sensitive Long-Horizon Candidate Evaluation for End-to-End Autonomous Driving
Overview
This research presents RiSE-Drive, a framework designed to evaluate trajectory candidates in end-to-end autonomous driving systems by assessing long-horizon outcome distributions rather than deterministic scores. The method performs candidate-conditioned latent future rollouts and ranks candidates using a tail-risk-aware decision criterion to handle delayed failures and safety-critical deviations. Experiments on NAVSIM-v1, NAVSIM-v2, and the closed-loop HUGSIM benchmark demonstrate state-of-the-art performance and improved robustness in candidate selection under uncertainty.
Generated from attributed reports · Updated 4 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalRiSE-Drive: Risk-Sensitive Long-Horizon Candidate Evaluation for End-to-End Autonomous Driving
This research presents RiSE-Drive, a framework designed to evaluate trajectory candidates in end-to-end autonomous driving systems by assessing long-horizon outcome distributions rather than deterministic scores. The method performs candidate-conditioned latent future rollouts and ranks candidates using a tail-risk-aware decision criterion to handle delayed failures and safety-critical deviations. Experiments on NAVSIM-v1, NAVSIM-v2, and the closed-loop HUGSIM benchmark demonstrate state-of-the-art performance and improved robustness in candidate selection under uncertainty.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).