235 citations · 850 across the 47 of their papers we have counts for
12 papers · 1 filter
FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs
Luke Rowe, Martin Ethier, Eli-Henry Dykhne +1
Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint,…
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
Sunsheng Gu, Vahdat Abdelzad, Krzysztof Czarnecki
Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models' predictions. However, such insights need to be interpreted by a human observer…
A Closer Look at Robustness to L-infinity and Spatial Perturbations and their Composition
Luke Rowe, Benjamin Thérien, Krzysztof Czarnecki +1
In adversarial machine learning, the popular threat model has been the focus of much previous work. While this mathematical definition of imperceptibility successfull…
Interpretable Deep Tracking
Benjamin Thérien, Krzysztof Czarnecki
Imagine experiencing a crash as the passenger of an autonomous vehicle. Wouldn't you want to know why it happened? Current end-to-end optimizable deep neural networks (DNNs) in 3D…
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
Chengjie Huang, Van Duong Nguyen, Vahdat Abdelzad +5
3D object detection is an essential part of automated driving, and deep neural networks (DNNs) have achieved state-of-the-art performance for this task. However, deep models are no…
A Safety Assurable Human-Inspired Perception Architecture
Rick Salay, Krzysztof Czarnecki
Although artificial intelligence-based perception (AIP) using deep neural networks (DNN) has achieved near human level performance, its well-known limitations are obstacles to the…
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki
Self-supervised learning (SSL) is an emerging technique that has been successfully employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) for more…
A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation
Scott Larter, Rodrigo Queiroz, Sean Sedwards +2
Modelling pedestrian behavior is crucial in the development and testing of autonomous vehicles. In this work, we present a hierarchical pedestrian behavior model that generates hig…
LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection
Matthew Pitropov, Chengjie Huang, Vahdat Abdelzad +2
The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance. Howeve…
A Driver-Vehicle Model for ADS Scenario-based Testing
Rodrigo Queiroz, Divit Sharma, Ricardo Caldas +4
Scenario-based testing for automated driving systems (ADS) must be able to simulate traffic scenarios that rely on interactions with other vehicles. Although many languages for hig…
If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components
Boyue Caroline Hu, Lina Marsso, Krzysztof Czarnecki +3
Machine Vision Components (MVC) are becoming safety-critical. Assuring their quality, including safety, is essential for their successful deployment. Assurance relies on the availa…
Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning
Jaeyoung Lee, Sean Sedwards, Krzysztof Czarnecki
We consider the challenge of finding a deterministic policy for a Markov decision process that uniformly (in all states) maximizes one reward subject to a probabilistic constraint…