59 citations · 347 across the 105 of their papers we have counts for
6 papers · 1 filter
Recurrent Attentive Neural Process for Sequential Data
Shenghao Qin, Jiacheng Zhu, Jimmy Qin +2
Neural processes (NPs) learn stochastic processes and predict the distribution of target output adaptively conditioned on a context set of observed input-output pairs. Furthermore,…
Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process
Jiacheng Zhu, Shenghao Qin, Wenshuo Wang +1
Predicting surrounding vehicle behaviors are critical to autonomous vehicles when negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious train…
Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
Weiyang Zhang, Wenshuo Wang, Ding Zhao
Generating multi-vehicle interaction scenarios can benefit motion planning and decision making of autonomous vehicles when on-road data is insufficient. This paper presents an effi…
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
Wenhao Ding, Mengdi Xu, Ding Zhao
Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplicat…
Active Learning for Risk-Sensitive Inverse Reinforcement Learning
Rui Chen, Wenshuo Wang, Zirui Zhao +1
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution. This assum…
How to Evaluate Proving Grounds for Self-Driving? A Quantitative Approach
Rui Chen, Mansur Arief, Weiyang Zhang +1
Proving ground has been a critical component in testing and validation for Connected and Automated Vehicles (CAV). Although quite a few world-class testing facilities have been und…