235 citations · 850 across the 47 of their papers we have counts for
14 papers · 1 filter
Efficacy of Pixel-Level OOD Detection for Semantic Segmentation
Matt Angus, Krzysztof Czarnecki, Rick Salay
The detection of out of distribution samples for image classification has been widely researched. Safety critical applications, such as autonomous driving, would benefit from the a…
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Vahdat Abdelzad, Krzysztof Czarnecki, Rick Salay +3
Deep neural networks achieve superior performance in challenging tasks such as image classification. However, deep classifiers tend to incorrectly classify out-of-distribution (OOD…
Out-of-distribution Detection in Classifiers via Generation
Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad +3
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples. For their real-world deployments, detecting out-of-dis…
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
Braden Hurl, Robin Cohen, Krzysztof Czarnecki +1
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communica…
MLOD: A multi-view 3D object detection based on robust feature fusion method
Jian Deng, Krzysztof Czarnecki
This paper presents Multi-view Labelling Object Detector (MLOD). The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection framewo…
Design Space of Behaviour Planning for Autonomous Driving
Marko Ilievski, Sean Sedwards, Ashish Gaurav +6
We explore the complex design space of behaviour planning for autonomous driving. Design choices that successfully address one aspect of behaviour planning can critically constrain…
A Micro-Objective Perspective of Reinforcement Learning
Changjian Li, Krzysztof Czarnecki
The standard reinforcement learning (RL) formulation considers the expectation of the (discounted) cumulative reward. This is limiting in applications where we are concerned with n…
FANTrack: 3D Multi-Object Tracking with Feature Association Network
Erkan Baser, Venkateshwaran Balasubramanian, Prarthana Bhattacharyya +1
We propose a data-driven approach to online multi-object tracking (MOT) that uses a convolutional neural network (CNN) for data association in a tracking-by-detection framework. Th…
Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
Braden Hurl, Krzysztof Czarnecki, Steven Waslander
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed wor…
Analysis of Confident-Classifiers for Out-of-distribution Detection
Sachin Vernekar, Ashish Gaurav, Taylor Denouden +4
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distributio…
Learning a Lattice Planner Control Set for Autonomous Vehicles
Ryan De Iaco, Stephen L. Smith, Krzysztof Czarnecki
This paper introduces a method to compute a sparse lattice planner control set that is suited to a particular task by learning from a representative dataset of vehicle paths. To do…
A behavior driven approach for sampling rare event situations for autonomous vehicles
Atrisha Sarkar, Krzysztof Czarnecki
Performance evaluation of urban autonomous vehicles requires a realistic model of the behavior of other road users in the environment. Learning such models from data involves colle…
Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving
Krzysztof Czarnecki, Rick Salay
Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation. This position paper identifies (1) perceptual unce…
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving
Jaeyoung Lee, Aravind Balakrishnan, Ashish Gaurav +2
Machine learning can provide efficient solutions to the complex problems encountered in autonomous driving, but ensuring their safety remains a challenge. A number of authors have…