Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2403.12176v2
• Date: Tue, 7 May 2024 22:55:45 GMT
• Title: Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
• Authors: Shahin Atakishiyev, Mohammad Salameh, Randy Goebel,
• Abstract summary: The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles. A lack of interpretability in real-time decisions with contemporary learning methods impedes user trust and attenuates the widespread deployment and commercialization of such vehicles. This survey seeks to answer the question: When and how can explanations improve safety of end-to-end autonomous driving?
• Score: 4.1636282808157254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The end-to-end learning pipeline is gradually creating a paradigm shift in the ongoing development of highly autonomous vehicles, largely due to advances in deep learning, the availability of large-scale training datasets, and improvements in integrated sensor devices. However, a lack of interpretability in real-time decisions with contemporary learning methods impedes user trust and attenuates the widespread deployment and commercialization of such vehicles. Moreover, the issue is exacerbated when these cars are involved in or cause traffic accidents. Such drawback raises serious safety concerns from societal and legal perspectives. Consequently, explainability in end-to-end autonomous driving is essential to build trust in vehicular automation. However, the safety and explainability aspects of end-to-end driving have generally been investigated disjointly by researchers in today's state of the art. This survey aims to bridge the gaps between these topics and seeks to answer the following research question: When and how can explanations improve safety of end-to-end autonomous driving? In this regard, we first revisit established safety and state-of-the-art explainability techniques in end-to-end driving. Furthermore, we present three critical case studies and show the pivotal role of explanations in enhancing self-driving safety. Finally, we describe insights from empirical studies and reveal potential value, limitations, and caveats of practical explainable AI methods with respect to their safety assurance in end-to-end autonomous driving.

Related papers

• Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
  We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving. Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
  arXiv  Detail & Related papers  (2024-07-09T04:56:11Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• Incorporating Explanations into Human-Machine Interfaces for Trust and Situation Awareness in Autonomous Vehicles [4.1636282808157254]
  We study the role of explainable AI and human-machine interface jointly in building trust in vehicle autonomy. We present a situation awareness framework for calibrating users' trust in self-driving behavior.
  arXiv  Detail & Related papers  (2024-04-10T23:02:13Z)
• Evaluation of Safety Constraints in Autonomous Navigation with Deep Reinforcement Learning [62.997667081978825]
  We compare two learnable navigation policies: safe and unsafe. The safe policy takes the constraints into the account, while the other does not. We show that the safe policy is able to generate trajectories with more clearance (distance to the obstacles) and makes less collisions while training without sacrificing the overall performance.
  arXiv  Detail & Related papers  (2023-07-27T01:04:57Z)
• Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey [9.385936248154987]
  End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with modular systems. Recent developments in End-to-End autonomous driving are analyzed, and research is categorized based on underlying principles. This paper assesses the state-of-the-art, identifies challenges, and explores future possibilities.
  arXiv  Detail & Related papers  (2023-07-10T07:00:06Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)
• Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving [104.32241082170044]
  We study a new task, safety-aware motion prediction with unseen vehicles for autonomous driving. Unlike the existing trajectory prediction task for seen vehicles, we aim at predicting an occupancy map. Our approach is the first one that can predict the existence of unseen vehicles in most cases.
  arXiv  Detail & Related papers  (2021-09-03T13:33:33Z)
• Explainability of vision-based autonomous driving systems: Review and challenges [33.720369945541805]
  The need for explainability is strong in driving, a safety-critical application. This survey gathers contributions from several research fields, namely computer vision, deep learning, autonomous driving, explainable AI (X-AI)
  arXiv  Detail & Related papers  (2021-01-13T19:09:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.