Research in machine learning for autonomous driving (AD) is a constantly evolving field as researchers strive to build a Level 5 autonomous driving system. However, current benchmarks for such learning algorithms do not satisfactorily allow researchers to evaluate and compare performance across safety-critical metrics such as generalizability, out-of-distribution performance, etc. Reasons for this include the expensive nature of data collection from the real-world for autonomous driving and the limitations of software tools currently available for autonomous driving simulators. We develop a pipeline that allows for automatic generation of new town maps for simulator environments from OpenStreetMap [Haklay and Weber, 2008]. We demonstrate that our pipeline is capable of generating towns that, when perceived via LiDAR , share similar footprint to real-world gathered datasets like NuScenes [Caesar et al., 2020]. Additionally, we learn a realistic noise augmentation via Conditional Adversarial Networks [Isola et al., 2017] to further improve the similarity with real-world LiDAR . Our pipeline allows researchers for the first time to benchmark at scale various AD agents, both in-distribution, and out-of-distribution.
Avishek Mondal, Panagiotis Tigas, Yarin Gal
Machine Learning for Autonomous Driving Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. [Paper]