Guest Editorial Special Issue on Sensing and Machine Learning for Automotive Perception

Abstract

There has been tremendous interest in self-driving and advanced driver assistance systems for automotives over the recent past. According to market predictions, achieving advanced levels of autonomous driving may still be significantly far from large-scale commercial deployment. One of the challenges is to obtain reliable environmental perception from onboard automotive sensors, and possibly external sensors, to support safety-critical driving. Automotive perception includes processed and learned information from multimodality sensors like lidar, camera, ultrasonic, and radar. Conventionally, this sensor information has been supporting functions like emergency braking, adaptive cruise control, and self-parking. This Special Issue explores advances in sensors, sensor system architectures, data processing, and machine learning for automotive perception. This Special Issue also aims to bridge the traditional model-based automotive sensing field with the rapidly emerging data-driven field that uses machine learning methods and focuses on feature representation for high-level semantic understanding. Driven by the efforts on automotive sensor hardware platforms and open datasets, vision-inspired deep learning has shown great potential to achieve state-of-the-art performance and yield better results than traditional signal processing methods in multiobject detection and tracking, simultaneous localization and mapping, multimodal sensor fusion, scene understanding, and interference mitigation. This Special Issue highlights advances in machine learning architectures and methods for automotive perception, alongside performance evaluation methodologies and field test results.