97 citations · 103 across the 5 of their papers we have counts for
7 papers
Timealign: A multi-modal object detection method for time misalignment fusing in autonomous driving
Zhihang Song, Lihui Peng, Jianming Hu +2
The multi-modal perception methods are thriving in the autonomous driving field due to their better usage of complementary data from different sensors. Such methods depend on calib…
Few-Shot Testing of Autonomous Vehicles with Scenario Similarity Learning
Shu Li, Honglin He, Jingxuan Yang +3
Testing and evaluation are critical to the development and deployment of autonomous vehicles (AVs). Given the rarity of safety-critical events such as crashes, millions of tests ar…
Dynamically Expanding Capacity of Autonomous Driving with Near-Miss Focused Training Framework
Ziyuan Yang, Zhaoyang Li, Jianming Hu +1
The long-tail distribution of real driving data poses challenges for training and testing autonomous vehicles (AV), where rare yet crucial safety-critical scenarios are infrequent.…
Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning
Jingxuan Yang, Ruoxuan Bai, Haoyuan Ji +3
The assessment of safety performance plays a pivotal role in the development and deployment of connected and automated vehicles (CAVs). A common approach involves designing testing…
Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
Shu Li, Jingxuan Yang, Honglin He +3
Testing and evaluating the safety performance of autonomous vehicles (AVs) is essential before the large-scale deployment. Practically, the number of testing scenarios permissible…
Synthetic Datasets for Autonomous Driving: A Survey
Zhihang Song, Zimin He, Xingyu Li +8
Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep…
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning
Haoyi Niu, Jianming Hu, Zheyu Cui +1
How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous drivin…