Search Results for author: Peter Du

Found 8 papers, 0 papers with code

Conveying Autonomous Robot Capabilities through Contrasting Behaviour Summaries

no code implementations1 Apr 2023 Peter Du, Surya Murthy, Katherine Driggs-Campbell

In this paper we present an adaptive search method for efficiently generating contrasting behaviour summaries with support for continuous state and action spaces.

Adaptive Failure Search Using Critical States from Domain Experts

no code implementations1 Apr 2023 Peter Du, Katherine Driggs-Campbell

Adaptive Stress Testing (AST) is one such method that poses the problem of failure search as a Markov decision process and uses reinforcement learning techniques to find high probability failures.

Autonomous Driving

CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)

no code implementations8 Jul 2022 Yuan Shen, Niviru Wijayaratne, Pranav Sriram, Aamir Hasan, Peter Du, Katherine Driggs-Campbell

In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.

Driver Attention Monitoring

CoCAtt: A Cognitive-Conditioned Driver Attention Dataset

no code implementations19 Nov 2021 Yuan Shen, Niviru Wijayaratne, Pranav Sriram, Aamir Hasan, Peter Du, Katie Driggs-Campbell

In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions.

Driver Attention Monitoring

Improving the Feasibility of Moment-Based Safety Analysis for Stochastic Dynamics

no code implementations11 Apr 2021 Peter Du, Katherine Driggs-Campbell, Roy Dong

We then reformulate the constraints of the optimization to mitigate the computational limitations associated with an increase in state dimensionality.

AutoPreview: A Framework for Autopilot Behavior Understanding

no code implementations25 Feb 2021 Yuan Shen, Niviru Wijayaratne, Peter Du, Shanduojiao Jiang, Katherine Driggs Campbell

The behavior of self driving cars may differ from people expectations, (e. g. an autopilot may unexpectedly relinquish control).

Self-Driving Cars

Online monitoring for safe pedestrian-vehicle interactions

no code implementations12 Oct 2019 Peter Du, Zhe Huang, Tianqi Liu, Ke Xu, Qichao Gao, Hussein Sibai, Katherine Driggs-Campbell, Sayan Mitra

As autonomous systems begin to operate amongst humans, methods for safe interaction must be investigated.

Robotics Multiagent Systems Signal Processing

Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation

no code implementations2 Aug 2019 Anthony Corso, Peter Du, Katherine Driggs-Campbell, Mykel J. Kochenderfer

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems.

Cannot find the paper you are looking for? You can Submit a new open access paper.