Krzysztof Czarnecki

research

∙ 11/27/2022

FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs

Predicting the future motion of road agents is a critical task in an aut...

Luke Rowe, et al. ∙

research

∙ 10/20/2022

XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection

Explainable AI (XAI) methods are frequently applied to obtain qualitativ...

Sunsheng Gu, et al. ∙

research

∙ 10/03/2022

Interpretable Deep Tracking

Imagine experiencing a crash as the passenger of an autonomous vehicle. ...

Benjamin Thérien, et al. ∙

research

∙ 09/28/2022

Out-of-Distribution Detection for LiDAR-based 3D Object Detection

3D object detection is an essential part of automated driving, and deep ...

Chengjie Huang, et al. ∙

research

∙ 06/28/2022

SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving

Self-supervised learning (SSL) is an emerging technique that has been su...

Prarthana Bhattacharyya, et al. ∙

research

∙ 06/01/2022

LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection

The estimation of uncertainty in robotic vision, such as 3D object detec...

Matthew Pitropov, et al. ∙

research

∙ 06/01/2022

A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation

Modelling pedestrian behavior is crucial in the development and testing ...

Scott Larter, et al. ∙

research

∙ 05/10/2022

A Safety Assurable Human-Inspired Perception Architecture

Although artificial intelligence-based perception (AIP) using deep neura...

Rick Salay, et al. ∙

research

∙ 05/05/2022

A Driver-Vehicle Model for ADS Scenario-based Testing

Scenario-based testing for automated driving systems (ADS) must be able ...

Rodrigo Queiroz, et al. ∙

research

∙ 02/08/2022

If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components

Machine Vision Components (MVC) are becoming safety-critical. Assuring t...

Boyue Caroline Hu, et al. ∙

research

∙ 01/20/2022

Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning

We consider the challenge of finding a deterministic policy for a Markov...

Jaeyoung Lee, et al. ∙

research

∙ 09/27/2021

A taxonomy of strategic human interactions in traffic conflicts

In order to enable autonomous vehicles (AV) to navigate busy traffic sit...

Atrisha Sarkar, et al. ∙

research

∙ 09/20/2021

Generalized dynamic cognitive hierarchy models for strategic driving behavior

While there has been an increasing focus on the use of game theoretic mo...

Atrisha Sarkar, et al. ∙

research

∙ 09/20/2021

I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames

A particular challenge for both autonomous and human driving is dealing ...

Maximilian Kahn, et al. ∙

research

∙ 08/30/2021

The missing link: Developing a safety case for perception components in automated driving

Safety assurance is a central concern for the development and societal a...

Rick Salay, et al. ∙

research

∙ 08/17/2021

Robustifying Controller Specifications of Cyber-Physical Systems Against Perceptual Uncertainty

Formal reasoning on the safety of controller systems interacting with pl...

Tsutomu Kobayashi, et al. ∙

research

∙ 01/07/2021

Self-Attention Based Context-Aware 3D Object Detection

Most existing point-cloud based 3D object detectors use convolution-like...

Prarthana Bhattacharyya, et al. ∙

research

∙ 10/10/2020

Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations

Autonomous Vehicles (AV) will transform transportation, but also the int...

Henry Chen, et al. ∙

research

∙ 09/21/2020

Solution Concepts in Hierarchical Games with Applications to Autonomous Driving

With autonomous vehicles (AV) set to integrate further into regular huma...

Atrisha Sarkar, et al. ∙

research

∙ 08/20/2020

Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations

We present Deformable PV-RCNN, a high-performing point-cloud based 3D ob...

Prarthana Bhattacharyya, et al. ∙

research

∙ 06/25/2020

The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches

Deep neural networks (DNNs) have become the de facto learning mechanism ...

Vahdat Abdelzad, et al. ∙

research

∙ 01/30/2020

Universally Safe Swerve Manoeuvres for Autonomous Driving

This paper characterizes safe following distances for on-road driving wh...

Ryan De Iaco, et al. ∙

research

∙ 01/27/2020

Canadian Adverse Driving Conditions Dataset

The Canadian Adverse Driving Conditions (CADC) dataset was collected wit...

Matthew Pitropov, et al. ∙

research

∙ 11/07/2019

Efficacy of Pixel-Level OOD Detection for Semantic Segmentation

The detection of out of distribution samples for image classification ha...

Matt Angus, et al. ∙

research

∙ 10/23/2019

Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output

Deep neural networks achieve superior performance in challenging tasks s...

Vahdat Abdelzad, et al. ∙

research

∙ 10/09/2019

Out-of-distribution Detection in Classifiers via Generation

By design, discriminatively trained neural network classifiers produce r...

Sachin Vernekar, et al. ∙

research

∙ 09/17/2019

TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data

Inter-vehicle communication for autonomous vehicles (AVs) stands to prov...

Braden Hurl, et al. ∙

research

∙ 09/09/2019

MLOD: A multi-view 3D object detection based on robust feature fusion method

This paper presents Multi-view Labelling Object Detector (MLOD). The det...

Jian Deng, et al. ∙

research

∙ 08/21/2019

Design Space of Behaviour Planning for Autonomous Driving

We explore the complex design space of behaviour planning for autonomous...

Marko Ilievski, et al. ∙

research

∙ 05/24/2019

Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective

The standard reinforcement learning (RL) formulation considers the expec...

Changjian Li, et al. ∙

research

∙ 05/07/2019

FANTrack: 3D Multi-Object Tracking with Feature Association Network

We propose a data-driven approach to online multi-object tracking (MOT) ...

Erkan Baser, et al. ∙

research

∙ 05/01/2019

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for ...

Braden Hurl, et al. ∙

research

∙ 04/27/2019

Analysis of Confident-Classifiers for Out-of-distribution Detection

Discriminatively trained neural classifiers can be trusted, only when th...

Sachin Vernekar, et al. ∙

research

∙ 03/05/2019

Learning a Lattice Planner Control Set for Autonomous Vehicles

In this paper, we introduce a method to compute a sparse lattice planner...

Ryan De Iaco, et al. ∙

research

∙ 03/04/2019

A behavior driven approach for sampling rare event situations for autonomous vehicles

Performance evaluation of urban autonomous vehicles requires a realistic...

Atrisha Sarkar, et al. ∙

research

∙ 03/03/2019

Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving

Perception is a safety-critical function of autonomous vehicles and mach...

Krzysztof Czarnecki, et al. ∙

research

∙ 02/11/2019

WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving

Machine learning can provide efficient solutions to the complex problems...

Jaeyoung Lee, et al. ∙

research

∙ 12/06/2018

Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance

There is an increasingly apparent need for validating the classification...

Taylor Denouden, et al. ∙

research

∙ 11/27/2018

Calibrating Uncertainties in Object Localization Task

In many safety-critical applications such as autonomous driving and surg...

Buu Phan, et al. ∙

research

∙ 11/21/2018

Urban Driving with Multi-Objective Deep Reinforcement Learning

Autonomous driving is a challenging domain that entails multiple aspects...

Changjian Li, et al. ∙

research

∙ 08/05/2018

Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262

The use of machine learning (ML) is on the rise in many sectors of softw...

Rick Salay, et al. ∙

research

∙ 07/23/2018

Clafer: Lightweight Modeling of Structure, Behaviour, and Variability

Embedded software is growing fast in size and complexity, leading to int...

Paulius Juodisius, et al. ∙

research

∙ 07/16/2018

Unlimited Road-scene Synthetic Annotation (URSA) Dataset

In training deep neural networks for semantic segmentation, the main lim...

Matt Angus, et al. ∙

research

∙ 09/07/2017

An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software

Machine learning (ML) plays an ever-increasing role in advanced automoti...

Rick Salay, et al. ∙

research

∙ 06/26/2017

Relating Complexity-theoretic Parameters with SAT Solver Performance

Over the years complexity theorists have proposed many structural parame...

Edward Zulkoski, et al. ∙

research

∙ 06/17/2015

SAT-based Analysis of Large Real-world Feature Models is Easy

Modern conflict-driven clause-learning (CDCL) Boolean SAT solvers provid...

Jia Hui Liang, et al. ∙

Krzysztof Czarnecki

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