Sensing and Machine Learning for Automotive Perception: A Review
Ashish Pandharipande, Chih‐Hong Cheng, Justin Dauwels, Sevgi Zübeyde Gürbüz, Javier Ibañez‐Guzmán, Guofa Li, Andrea Piazzoni, Pu Wang, Avik Santra
Abstract
Automotive perception involves understanding the external driving environment and the internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving high levels of safety and autonomy in driving. This article provides an overview of different sensor modalities, such as cameras, radars, and light detection and ranging (LiDAR) used commonly for perception, along with the associated data processing techniques. Critical aspects of perception are considered, such as architectures for processing data from single or multiple sensor modalities, sensor data processing algorithms and the role of machine learning techniques, methodologies for validating the performance of perception systems, and safety. The technical challenges for each aspect are analyzed, emphasizing machine learning approaches, given their potential impact on improving perception. Finally, future research opportunities in automotive perception for their wider deployment are outlined.