424 citations · 860 across the 53 of their papers we have counts for
10 papers · 1 filter
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…
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…
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning
Balint Gyevnar, Massimiliano Tamborski, Cheng Wang +3
Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and q…
Verifiable Goal Recognition for Autonomous Driving with Occlusions
Cillian Brewitt, Massimiliano Tamborski, Cheng Wang +1
Goal recognition (GR) involves inferring the goals of other vehicles, such as a certain junction exit, which can enable more accurate prediction of their future behaviour. In auton…
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…
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…
GRIT: Fast, Interpretable, and Verifiable Goal Recognition with Learned Decision Trees for Autonomous Driving
Cillian Brewitt, Balint Gyevnar, Samuel Garcin +1
It is important for autonomous vehicles to have the ability to infer the goals of other vehicles (goal recognition), in order to safely interact with other vehicles and predict the…
PILOT: Efficient Planning by Imitation Learning and Optimisation for Safe Autonomous Driving
Henry Pulver, Francisco Eiras, Ludovico Carozza +3
Achieving a proper balance between planning quality, safety and efficiency is a major challenge for autonomous driving. Optimisation-based motion planners are capable of producing…
Interpretable Goal-based Prediction and Planning for Autonomous Driving
Stefano V. Albrecht, Cillian Brewitt, John Wilhelm +4
We propose an integrated prediction and planning system for autonomous driving which uses rational inverse planning to recognise the goals of other vehicles. Goal recognition infor…
A Two-Stage Optimization-based Motion Planner for Safe Urban Driving
Francisco Eiras, Majd Hawasly, Stefano V. Albrecht +1
Recent road trials have shown that guaranteeing the safety of driving decisions is essential for the wider adoption of autonomous vehicle technology. One promising direction is to…