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

• URL: http://arxiv.org/abs/2403.12176v3
• Date: Tue, 14 Jan 2025 22:44:14 GMT
• Title: Safety Implications of Explainable Artificial Intelligence in End-to-End Autonomous Driving
• Authors: Shahin Atakishiyev, Mohammad Salameh, Randy Goebel,
• Abstract summary: This paper seeks to answer the question: What are safety implications of explanations in end-to-end autonomous driving? We first revisit established safety and explainability concepts in end-to-end driving. Furthermore, we show the pivotal role of explanations in enhancing self-driving safety.
• Score: 4.1636282808157254
• License:
• 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 explainability 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. Consequently, explainability in end-to-end autonomous driving is essential to build trust in vehicular automation. With that said, automotive researchers have not yet rigorously explored safety benefits and consequences of explanations in end-to-end autonomous driving. This paper aims to bridge the gaps between these topics and seeks to answer the following research question: What are safety implications of explanations in end-to-end autonomous driving? In this regard, we first revisit established safety and explainability concepts 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 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)
• 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)
• FBLNet: FeedBack Loop Network for Driver Attention Prediction [50.936478241688114]
  Nonobjective driving experience is difficult to model, so a mechanism simulating the driver experience accumulation procedure is absent in existing methods. We propose a FeedBack Loop Network (FBLNet), which attempts to model the driving experience accumulation procedure. Our model exhibits a solid advantage over existing methods, achieving an outstanding performance improvement on two driver attention benchmark datasets.
  arXiv  Detail & Related papers  (2022-12-05T08:25:09Z)
• 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.