457 citations · 845 across the 36 of their papers we have counts for
7 papers · 1 filter
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning
Zhong Cao, Shaobing Xu, Songan Zhang +2
Safeguard functions such as those provided by advanced emergency braking (AEB) can provide another layer of safety for autonomous vehicles (AV). A smart safeguard function should a…
Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss
Yuanxin Zhong, Minghan Zhu, Huei Peng
Object detection and tracking is a key task in autonomy. Specifically, 3D object detection and tracking have been an emerging hot topic recently. Although various methods have been…
SUPER: A Novel Lane Detection System
Pingping Lu, Chen Cui, Shaobing Xu +2
AI-based lane detection algorithms were actively studied over the last few years. Many have demonstrated superior performance compared with traditional feature-based methods. The a…
Combined Eco-Routing and Power-Train Control of Plug-In Hybrid Electric Vehicles in Transportation Networks
Arian Houshmand, Christos G. Cassandras, Nan Zhou +3
We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously c…
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles
Songan Zhang, Huei Peng, Subramanya Nageshrao +1
Deep reinforcement learning methods have been widely used in recent years for autonomous vehicle's decision-making. A key issue is that deep neural networks can be fragile to adver…
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
Lu Wen, Jingliang Duan, Shengbo Eben Li +2
Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to its potential to solve complex classification and control problems. However, existing RL…
Monocular Depth Prediction through Continuous 3D Loss
Minghan Zhu, Maani Ghaffari, Yuanxin Zhong +4
This paper reports a new continuous 3D loss function for learning depth from monocular images. The dense depth prediction from a monocular image is supervised using sparse LIDAR po…