185 citations · 367 across the 8 of their papers we have counts for
9 papers · 1 filter
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…
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…
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving
Hengli Wang, Peide Cai, Rui Fan +2
With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance. Nevertheless, mo…
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation
Rui Fan, Hengli Wang, Peide Cai +4
Collision-free space detection is a critical component of autonomous vehicle perception. The state-of-the-art algorithms are typically based on supervised learning. The performance…
DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks
Peide Cai, Hengli Wang, Yuxiang Sun +1
Traditional decision and planning frameworks for self-driving vehicles (SDVs) scale poorly in new scenarios, thus they require tedious hand-tuning of rules and parameters to mainta…