185 citations · 689 across the 15 of their papers we have counts for
15 papers
Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning
Peide Cai, Sukai Wang, Hengli Wang +1
Autonomous driving in urban crowds at unregulated intersections is challenging, where dynamic occlusions and uncertain behaviors of other vehicles should be carefully considered. T…
R-PCC: A Baseline for Range Image-based Point Cloud Compression
Sukai Wang, Jianhao Jiao, Peide Cai +1
In autonomous vehicles or robots, point clouds from LiDAR can provide accurate depth information of objects compared with 2D images, but they also suffer a large volume of data, wh…
DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should a…
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
Peide Cai, Hengli Wang, Huaiyang Huang +2
Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computati…
SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection
Hengli Wang, Rui Fan, Peide Cai +1
Freespace detection is a fundamental component of autonomous driving perception. Recently, deep convolutional neural networks (DCNNs) have achieved impressive performance for this…
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation
Hengli Wang, Peide Cai, Yuxiang Sun +2
Recently, deep-learning based approaches have achieved impressive performance for autonomous driving. However, end-to-end vision-based methods typically have limited interpretabili…