Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving

[PDF] arxiv.org

Memorize What Matters: Emergent Scene Decomposition from Multitraverse

Y Li, Z Wang, Y Wang, Z Yu, Z Gojcic, M Pavone… - arXiv preprint arXiv …, 2024 - arxiv.org
Humans naturally retain memories of permanent elements, while ephemeral moments often
slip through the cracks of memory. This selective retention is crucial for robotic perception …
被引用次数：2 相关文章 所有 2 个版本
[PDF] arxiv.org

SLEDGE: Synthesizing Simulation Environments for Driving Agents with Generative Models

K Chitta, D Dauner, A Geiger - arXiv preprint arXiv:2403.17933, 2024 - arxiv.org
SLEDGE is the first generative simulator for vehicle motion planning trained on real-world
driving logs. Its core component is a learned model that is able to generate agent bounding …
相关文章 所有 2 个版本
[PDF] arxiv.org

NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving

W Ljungbergh, A Tonderski, J Johnander… - arXiv preprint arXiv …, 2024 - arxiv.org
We present a versatile NeRF-based simulator for testing autonomous driving (AD) software
systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation …
被引用次数：1 相关文章 所有 3 个版本
[PDF] arxiv.org

NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking

D Dauner, M Hallgarten, T Li, X Weng, Z Huang… - arXiv preprint arXiv …, 2024 - arxiv.org
Benchmarking vision-based driving policies is challenging. On one hand, open-loop
evaluation with real data is easy, but these results do not reflect closed-loop performance …
相关文章 所有 2 个版本