10 citations · 16 across the 8 of their papers we have counts for
8 papers
Learning to View: Decision Transformers for Active Object Detection
Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande +4
Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environm…
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu +8
One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result i…
Spatiotemporal Transformer Attention Network for 3D Voxel Level Joint Segmentation and Motion Prediction in Point Cloud
Zhensong Wei, Xuewei Qi, Zhengwei Bai +6
Environment perception including detection, classification, tracking, and motion prediction are key enablers for automated driving systems and intelligent transportation applicatio…
Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR
Zhengwei Bai, Saswat Priyadarshi Nayak, Xuanpeng Zhao +6
Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems. How…
Infrastructure-Based Object Detection and Tracking for Cooperative Driving Automation: A Survey
Zhengwei Bai, Guoyuan Wu, Xuewei Qi +3
Object detection plays a fundamental role in enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing safety, mobility, and sust…
Cyber Mobility Mirror for Enabling Cooperative Driving Automation in Mixed Traffic: A Co-Simulation Platform
Zhengwei Bai, Guoyuan Wu, Xuewei Qi +3
Endowed with automation and connectivity, Connected and Automated Vehicles are meant to be a revolutionary promoter for Cooperative Driving Automation. Nevertheless, CAVs need high…
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Mengdi Xu, Peide Huang, Fengpei Li +6
Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces wher…
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning
Zhensong Wei, Yu Jiang, Xishun Liao +5
This paper presented a deep reinforcement learning method named Double Deep Q-networks to design an end-to-end vision-based adaptive cruise control (ACC) system. A simulation envir…