Industry Practices for Challenging Autonomous Driving Systems with
Critical Scenarios
- URL: http://arxiv.org/abs/2305.14146v1
- Date: Tue, 23 May 2023 15:13:11 GMT
- Title: Industry Practices for Challenging Autonomous Driving Systems with
Critical Scenarios
- Authors: Qunying Song, Emelie Engstr\"om, Per Runeson
- Abstract summary: Testing autonomous driving systems for safety and reliability is extremely complex.
There are several proposed methods and tools for critical scenario identification.
The industry practices, such as the selection, implementation, and limitations of the approaches, are not well understood.
- Score: 2.42477526148542
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Testing autonomous driving systems for safety and reliability is extremely
complex. A primary challenge is identifying the relevant test scenarios,
especially the critical ones that may expose hazards or risks of harm to
autonomous vehicles and other road users. There are several proposed methods
and tools for critical scenario identification, while the industry practices,
such as the selection, implementation, and limitations of the approaches, are
not well understood. In this study, we conducted 10 interviews with 13
interviewees from 7 companies in autonomous driving in Sweden. We used thematic
modeling to analyse and synthesize the interview data. We found there are
little joint efforts in the industry to explore different approaches and tools,
and every approach has its own limitations and weaknesses. To that end, we
recommend combining different approaches available, collaborating among
different stakeholders, and continuously learning the field of critical
scenario identification and testing. The contributions of our study are the
exploration and synthesis of the industry practices and related challenges for
critical scenario identification and testing, and the potential increase of the
industry relevance for future studies in related topics.
Related papers
- Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices.
This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
arXiv Detail & Related papers (2024-11-04T09:21:00Z) - 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) - On STPA for Distributed Development of Safe Autonomous Driving: An Interview Study [0.7851536646859475]
System-Theoretic Process Analysis (STPA) is a novel method applied in safety-related fields like defense and aerospace.
STPA assumes prerequisites that are not fully valid in the automotive system engineering with distributed system development and multi-abstraction design levels.
This can be seen as a maintainability challenge in continuous development and deployment.
arXiv Detail & Related papers (2024-03-14T15:56:02Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - Social Interaction-Aware Dynamical Models and Decision Making for
Autonomous Vehicles [20.123965317836106]
Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of research.
It focuses on the development of autonomous vehicles that are capable of interacting safely and efficiently with human road users.
This is a challenging task, as it requires the autonomous vehicle to be able to understand and predict the behaviour of human road users.
arXiv Detail & Related papers (2023-10-29T03:43:50Z) - The Thousand Faces of Explainable AI Along the Machine Learning Life
Cycle: Industrial Reality and Current State of Research [37.69303106863453]
Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning lifecycle.
Our findings also confirm that more efforts are needed to enable also non-expert users' interpretation and understanding of opaque AI models.
arXiv Detail & Related papers (2023-10-11T20:45:49Z) - Clustering-based Criticality Analysis for Testing of Automated Driving
Systems [0.18416014644193066]
This paper focuses on the the goal to reduce the scenario set by clustering concrete scenarios from a single logical scenario.
By employing clustering techniques, redundant and uninteresting scenarios can be identified and eliminated, resulting in a representative scenario set.
arXiv Detail & Related papers (2023-06-22T08:36:20Z) - Machine Unlearning: A Survey [56.79152190680552]
A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning.
This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality.
No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios.
The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
arXiv Detail & Related papers (2023-06-06T10:18:36Z) - Finding Critical Scenarios for Automated Driving Systems: A Systematic
Literature Review [20.926088145784604]
We present the results of a systematic literature review in the context of autonomous driving.
We introduce a comprehensive taxonomy for critical scenario identification methods.
We also give an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020.
arXiv Detail & Related papers (2021-10-16T21:24:19Z) - Generating and Characterizing Scenarios for Safety Testing of Autonomous
Vehicles [86.9067793493874]
We propose efficient mechanisms to characterize and generate testing scenarios using a state-of-the-art driving simulator.
We use our method to characterize real driving data from the Next Generation Simulation (NGSIM) project.
We rank the scenarios by defining metrics based on the complexity of avoiding accidents and provide insights into how the AV could have minimized the probability of incurring an accident.
arXiv Detail & Related papers (2021-03-12T17:00:23Z)
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.