2023
,
Pandharipande, Ashish
,
Cheng, Chih-Hong
,
Dauwels, Justin
,
Gurbuz, Sevgi Z.
,
Ibanez-Guzman, Javier
,
Li, Guofa
,
Piazzoni, Andrea
,
Wang, Pu
,
Santra, Avik
Automotive perception involves understanding the external driving environment as well as 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 paper provides an overview of different sensor modalities like cameras, radars, and LiDARs used commonly for perception, along with the associated data processing techniques. Critical aspects in perception are considered, like 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.
2023
,
Santra, Avik
,
Pandharipande, Ashish
,
Wang, Pu Perry
,
Gurbuz, Sevgi Zubeyde
,
Ibañez-Guzmãn, Javier
,
Cheng, Chih-Hong
,
Dauwels, Justin
,
Li, Guofa
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.