Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions
Autonomous driving has achieved significant milestones in research and development over
the last decade. There is increasing interest in the field as the deployment of self-operating …
the last decade. There is increasing interest in the field as the deployment of self-operating …
A review of trustworthy and explainable artificial intelligence (xai)
The advancement of Artificial Intelligence (AI) technology has accelerated the development
of several systems that are elicited from it. This boom has made the systems vulnerable to …
of several systems that are elicited from it. This boom has made the systems vulnerable to …
GRIT: Fast, interpretable, and verifiable goal recognition with learned decision trees for autonomous driving
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 their future …
(goal recognition), in order to safely interact with other vehicles and predict their future …
Efficient speed planning for autonomous driving in dynamic environment with interaction point model
Safely interacting with other traffic participants is one of the core requirements for
autonomous driving, especially in intersections and occlusions. Most existing approaches …
autonomous driving, especially in intersections and occlusions. Most existing approaches …
Real-time heterogeneous road-agents trajectory prediction using hierarchical convolutional networks and multi-task learning
L Li, X Wang, D Yang, Y Ju, Z Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Trajectory prediction of heterogeneous road agents such as vehicles, cyclists, and
pedestrians in dense traffic plays an essential role in self-driving. Despite breakthroughs in …
pedestrians in dense traffic plays an essential role in self-driving. Despite breakthroughs in …
Explainable goal recognition: A framework based on weight of evidence
We introduce and evaluate an eXplainable goal recognition (XGR) model that uses the
Weight of Evidence (WoE) framework to explain goal recognition problems. Our model …
Weight of Evidence (WoE) framework to explain goal recognition problems. Our model …
Deep reinforcement learning for multi-agent interaction
The development of autonomous agents which can interact with other agents to accomplish
a given task is a core area of research in artificial intelligence and machine learning …
a given task is a core area of research in artificial intelligence and machine learning …
Verifiable goal recognition for autonomous driving with occlusions
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 …
junction exit, which can enable more accurate prediction of their future behaviour. In …
Pilot: Efficient planning by imitation learning and optimisation for safe autonomous driving
Achieving a proper balance between planning quality, safety and efficiency is a major
challenge for autonomous driving. Optimisation-based motion planners are capable of …
challenge for autonomous driving. Optimisation-based motion planners are capable of …
DiPA: probabilistic multi-modal interactive prediction for autonomous driving
A Knittel, M Hawasly, SV Albrecht… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
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 …
scenarios. Prediction must be fast, to support multiple requests from a planner exploring a …