Automotive Innovation - Call for Papers: Feature Topic on Testing of Highly Automated Vehicles

The landscape of autonomous vehicles is rapidly evolving, bringing unprecedented opportunities and challenges. As the industry strives towards the realization of safe and efficient autonomous transportation, the role of testing becomes paramount. The current autonomous driving systems have been embedded with a considerable amount of AI algorithms in either perception module or motion planning module, thus the burgeoning field of highly automated vehicle testing encompasses a spectrum of challenges, mainly test efficiency and test result reliability. Research on processing existing scenario datasets, defining realistic testing scenarios with trustworthy surrounding vehicles/pedestrians, expediating testing procedures amongst extremely condensed scenario library, evaluating the efficacy of testing methodologies across public road testing, closed-course testing, and simulation testing, etc. emerge. This feature topic aims to explore the critical importance and necessity of advanced testing methodologies (especially the simulation testing) in the development of highly automated vehicles. We invite researchers and experts to contribute to this discourse, shedding light on the future of testing in the highly automated vehicles’ domain.

Within the spectrum of highly automated vehicle testing, simulation testing stands out as a linchpin, enabling controlled and repeatable evaluations. The necessity for advancements in key technologies within this context is evident. Addressing the intricacies of testing scenario construction and generalization, risky scenario recognition, fault diagnosis, simulation credibility assessment, simulation toolchain development, and comprehensive testing evaluation is paramount. In the meanwhile, virtual-real fusion testing is also rapidly advancing. The evolving challenges in testing highly automated vehicles necessitate not only an understanding of current technological landscapes but also a proactive exploration of innovative solutions to propel the field forward.

This special issue encompasses a broad spectrum of topics related to the testing of highly automated vehicles. Authors are encouraged to submit original research, reviews, and case studies that focus on, but are not limited to, the following areas:

• Simulated Scenario-based Testing
• Generation of Test Scenarios
• Test Automation for Highly Automated Vehicles
• Fault Diagnosis for Highly Automated Vehicles
• Virtual-real Fusion Testing and Its Real-world Implications
• Methods for Assessing the Credibility of Simulation Testing Results
• Autonomous Driving Data Closed-Loop
• Establishment and Enhancement of Simulation Toolchains and Public Testing Platforms
• Novel Evaluation Methodologies for Comprehensive Autonomous Vehicle Testing

Contributions that bridge the gap between theoretical developments and practical applications are particularly welcomed. We invite researchers, engineers, and practitioners to share their insights and findings, fostering a deeper understanding of the current state and future directions in the testing of autonomous vehicles.

Important Dates Submission Deadline: June 30, 2024

Guest Editors
Dr. Jian Sun, Tongji University, China
Dr. Matthias Althoff, Technical University of Munich, Germany
Dr. Ding Zhao, Carnegie Mellon University, USA
Dr. Ye Tian, Tongji University, China
Dr. Xianbiao Hu, The Pennsylvania State University, USA

Submission Guidelines

The paper submission and review process will be managed through Automotive Innovation. Here are the submission guidelines:

• Please submit online via www.springer.com/42154 (this opens in a new tab), ensuring you select "Topical Collection: Testing of Highly Automated Vehicles".
• Submit papers as two separate docx. files: Blinded Manuscript (paper title, abstract, keywords, and full text), and Title Page (paper title, author affiliations, acknowledgments, and any other information pertaining to author identification).
• All manuscripts will undergo peer review, and evaluations will be based on criteria such as quality, originality, novelty, and relevance to the topic. If you encounter any issues, do not hesitate to contact the journal's editorial office via email: jai-editor@sae-china.org. (this opens in a new tab)