Challenges in Autonomous Vehicle Testing and Validation

By P. Koopman, Michael D. Wagner. 2016

663 citations recorded by the source corpus.

Subfields: Safety, Risk, Reliability and Quality, Software

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Software

1. Sherali Zeadally
2. Rasheed Hussain
3. Krzysztof Czarnecki
4. Rick Salay
5. Christian Kästner

Citations in the corpus

• On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “r less confident predictions, stop the autonomous control software and switch to a backup system or a degraded level of autonomy (e.g., full control by the driver) are considered for self-driving cars [48]. B. Decision Sciences In decision sciences applications, people are in the loop in a different ”
• Autonomous Vehicle Safety: An Interdisciplinary Challenge (2017, background): “But it also means things such as fail-over mission planning, finding a way to validate inductive-based learning strategies, providing resilience in the face of likely gaps in early-deployed system requirements, and having an appropriate safety certification strategy to demonstrate that a sufficient…”
• Autonomous Cars: Research Results, Issues, and Future Challenges (2019, background): “Koopman and Wagner believe that fault injection will outperform current techniques, such as traditional testing and validation and model V testing that are used in autonomous cars’ performance analysis [133].”
• Computing Systems for Autonomous Driving: State of the Art and Challenges (2020, background): “One reason is that machine learning learns from large amounts of data and stores the model in a complex set of weighted feature combinations, which is not intuitive or difficult to conduct thorough testing [214].”
• Using Machine Learning Safely in Automotive Software: An Assessment and Adaption of Software Process Requirements in ISO 26262 (2018, background): “In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role [33, 18].”
• Software Engineering for AI-Based Systems: A Survey (2021, methodology): “Software Engineering Management, Software Engineering Process, Software Engineering Models and Methods, Software Quality, Software Engineering Professional Practice, Software Engineering Economics, Computing Foundations, Mathematical Foundations, and Engineering Foundations.”
• A Survey of End-to-End Driving: Architectures and Training Methods (2020, background): “Hence, the problem of black-box testing is universally relevant for all autonomous driving models and has been discussed intensively over the years [121], [122], [127], [136], [137].”
• Survey on Scenario-Based Safety Assessment of Automated Vehicles (2020, background): “Consequently, the safety of Automated Vehicles (AVs) must be thoroughly tested before they can be launched on the market, which is a challenging task [3]–[5].”