19 citations · 33 across the 12 of their papers we have counts for
12 papers
Attacking Motion Planners Using Adversarial Perception Errors
Jonathan Sadeghi, Nicholas A. Lord, John Redford +1
Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics. These metrics s…
Comparison of Pedestrian Prediction Models from Trajectory and Appearance Data for Autonomous Driving
Anthony Knittel, Morris Antonello, John Redford +1
The ability to anticipate pedestrian motion changes is a critical capability for autonomous vehicles. In urban environments, pedestrians may enter the road area and create a high r…
An Active Learning Reliability Method for Systems with Partially Defined Performance Functions
Jonathan Sadeghi, Romain Mueller, John Redford
In engineering design, one often wishes to calculate the probability that the performance of a system is satisfactory under uncertainty. State of the art algorithms exist to solve…
DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht +2
Accurate prediction is important for operating an autonomous vehicle in interactive scenarios. Prediction must be fast, to support multiple requests from a planner exploring a rang…
Query-based Hard-Image Retrieval for Object Detection at Test Time
Edward Ayers, Jonathan Sadeghi, John Redford +2
There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory. In real-world applic…
Perspectives on the System-level Design of a Safe Autonomous Driving Stack
Majd Hawasly, Jonathan Sadeghi, Morris Antonello +3
Achieving safe and robust autonomy is the key bottleneck on the path towards broader adoption of autonomous vehicles technology. This motivates going beyond extrinsic metrics such…
Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?
Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello +9
Autonomous vehicles use a variety of sensors and machine-learned models to predict the behavior of surrounding road users. Most of the machine-learned models in the literature focu…
Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles
Morris Antonello, Mihai Dobre, Stefano V. Albrecht +2
Motion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a…
A Step Towards Efficient Evaluation of Complex Perception Tasks in Simulation
Jonathan Sadeghi, Blaine Rogers, James Gunn +4
There has been increasing interest in characterising the error behaviour of systems which contain deep learning models before deploying them into any safety-critical scenario. Howe…
Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
Josiah P. Hanna, Arrasy Rahman, Elliot Fosong +5
Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When the…
Imagining the Unseen: Learning a Distribution over Incomplete Images with Dense Latent Trees
Sebastian Kaltwang, Sina Samangooei, John Redford +1
Images are composed as a hierarchy of object parts. We use this insight to create a generative graphical model that defines a hierarchical distribution over image parts. Typically,…
A Dataset for Lane Instance Segmentation in Urban Environments
Brook Roberts, Sebastian Kaltwang, Sina Samangooei +3
Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs. This work addresses the current lack of data for determining…