TY - CHAP
T1 - Environmental Perception for Intelligent Vehicles
AU - Tang, Xiaoliang
AU - Li, Yuanxiang
AU - Wei, Xian
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Environmental Perception for Intelligent Vehicles (EPIV) generally focuses on the awareness and understanding of the driving environment around intelligent vehicles by various vehicle sensors. In recent years, a lot of excellent research has been conducted on developing novel methods and technologies for EPIV. This chapter overviews some of the main research topics in the field of EPIV. First, this chapter reviews various types of vehicle sensors which capture multimodal environmental information around intelligent vehicles and form the foundation for environmental perception. Second, this chapter focuses on data restoration and denoising technologies on camera and LiDAR sensors, which guarantees the quality of the data captured by the vehicle. Third, this chapter deals with methods on semantic segmentation, object detection and tracking with camera and LiDAR data, which play a central role in environmental understanding. Fourth, this chapter introduces technologies on location and mapping with multimodal sensor data, which is essential for the local path planning of intelligent vehicles. Finally, this chapter discusses the research technologies on fusing multimodal environmental data, which represents the frontier of EPIV development.
AB - Environmental Perception for Intelligent Vehicles (EPIV) generally focuses on the awareness and understanding of the driving environment around intelligent vehicles by various vehicle sensors. In recent years, a lot of excellent research has been conducted on developing novel methods and technologies for EPIV. This chapter overviews some of the main research topics in the field of EPIV. First, this chapter reviews various types of vehicle sensors which capture multimodal environmental information around intelligent vehicles and form the foundation for environmental perception. Second, this chapter focuses on data restoration and denoising technologies on camera and LiDAR sensors, which guarantees the quality of the data captured by the vehicle. Third, this chapter deals with methods on semantic segmentation, object detection and tracking with camera and LiDAR data, which play a central role in environmental understanding. Fourth, this chapter introduces technologies on location and mapping with multimodal sensor data, which is essential for the local path planning of intelligent vehicles. Finally, this chapter discusses the research technologies on fusing multimodal environmental data, which represents the frontier of EPIV development.
UR - https://www.scopus.com/pages/publications/85166918607
U2 - 10.1007/978-3-031-06780-8_3
DO - 10.1007/978-3-031-06780-8_3
M3 - 章节
AN - SCOPUS:85166918607
T3 - Lecture Notes in Intelligent Transportation and Infrastructure
SP - 61
EP - 106
BT - Lecture Notes in Intelligent Transportation and Infrastructure
PB - Springer Nature
ER -