Autonomous Vehicle Perception: The Technology of Today and Tomorrow

Dear colleagues,

Autonomous driving (AD) and advanced driver assistance systems (ADAS) involve a typical “sense-plan-act” cycle that was initially developed in the context of robot control. The first and perhaps the most important step of this cycle is the sensing or “perception” of the surrounding environment using on-board and/or infrastructure sensors, which provides the necessary information for the subsequent planning and control steps. The sensors often include superfluous combinations of camera, Lidar, radar, and ultrasonic sensors, which need to be fused in an efficient manner to ensure a robust and fail-operational surround perception, often utilizing AI methods. This very same goal poses the biggest challenge for ADAS/AD systems, since perception systems require extensive calibration, testing, and validation to ensure that the related ADAS/AD functions behave as expected in a multitude of traffic scenarios. Conventional automotive testing and validation methods are neither suitable nor feasible for this purpose. It is therefore in the interest of developers and OEMs to ensure that ADAS/AD systems conform to reliability, compliance, performance, and fault-tolerance requirements in a cost effective and efficient manner. One major challenge for this, among others, is to find generally accepted key scenarios and their corresponding KPIs for the evaluation of automotive perception systems and the corresponding ADAS and AD functions. Given these facts, there is an increasing interest in novel testing, verification, and validation concepts.

This Special Issue aims to bring industry and academia together to exchange ideas, experiences, and research results on perception systems for ADAS/AD applications as well as related testing, verification, validation, and certification methodologies.

Prof. Dr. Selim Solmaz
Dr. Georg Stettinger
Guest Editors

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• novel perception system solutions for automated vehicles
• testing, verification, and validation of perception systems
• sensor models and simulation
• sensors and sensor fusion methodologies for autonomous driving
• datasets for development and verification of perception systems
• benchmarks and comparisons of perception system solutions
• use of perception for traffic prediction and control
• intention monitoring of vulnerable road users
• dependable machine learning and AI for automated driving
• standardization and certification of perception systems