Sensor Data Fusion for Autonomous and Connected Driving

Dear Colleagues,

Over the last decades, the development of advanced driver assistance systems (ADAS) has become a critical endeavor to reach different objectives: safety enhancement, mobility improvement, energy optimization and driver comfort. In order to tackle the first three objectives, considerable research focusing on autonomous driving has been carried out. Recent research and the development of highly automated driving aim to ultimately replace the driver’s actions with robotic functions. Partially automated driving will require co-pilot applications involving a combination of the above methods, algorithms and architectures. Such a system is built from complex, distributed and cooperative architectures with strong properties such as reliability and robustness. These properties must be maintained despite complex and degraded working conditions, including adverse weather conditions, fog or dust as perceived by sensors. This Special Issue will provide an overview of the recent research related to sensor and data fusion, information processing and merging, and fusion architecture for the cooperative perception and risk assessment needed for autonomous mobility means. Indeed, prior to ensuring a high level of safety in the deployment of autonomous driving applications, it is necessary to guarantee high-quality and real-time perception mechanisms. Therefore, research contributions concerning new automotive sensors, AI for semantic information generation and safe operation are welcome.

Dr. Dominique Gruyer
Dr. Olivier Orfila
Prof. Dr. Haihao Sun
Prof. Dr. Homayoun Najjaran
Guest Editors

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