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Data-Driven Risk Fields for Safer End-to-End Autonomous Driving

Paper recorded by Signals 4 on 2026-09-09 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10

Category: cs.CV · 计算机视觉 · first seen 2026-09-10

Abstract

Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities. Existing rule-based risk models provide interpretable safety priors, yet their absolute risk scores depend on handcrafted functions, coefficients, and thresholds. Learning-based risk representations reduce part of this manual design, but their super

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#106 most recent of 237 cs.CV papers we have recorded · ↑ newer: Enhanced Deformable Convolution with Center-invariant Offset and Edge- · ↓ older: Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstructio
Cite this page: Data-Driven Risk Fields for Safer End-to-End Autonomous Driving: the #106 most recent of 237 cs.CV papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/data-driven-risk-fields-for-safer-end-to-end-autonomous-driving.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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