A Survey of Automotive Radar and Lidar Signal Processing and Architectures

Abstract

In recent years, the development of Advanced Driver-Assistance Systems (ADASs) is driving the need for more reliable and precise on-vehicle sensing. Radar and lidar are crucial in this framework, since they allow sensing of vehicle’s surroundings. In such a scenario, it is necessary to master these sensing systems, and knowing their similarities and differences is important. Due to ADAS’s intrinsic real-time performance requirements, it is almost mandatory to be aware of the processing algorithms required by radar and lidar to understand what can be optimized and what actions can be taken to approach the real-time requirement. This review aims to present state-of-the-art radar and lidar technology, mainly focusing on modulation schemes and imaging systems, highlighting their weaknesses and strengths. Then, an overview of the sensor data processing algorithms is provided, with some considerations on what type of algorithms can be accelerated in hardware, pointing to some implementations from the literature. In conclusion, the basic concepts of sensor fusion are presented, and a comparison between radar and lidar is performed.

Multi-Environment Adaptive Fast Constant False Alarm Detection Algorithm Optimization Strategy

“…Among the 13 data obtained, some may contain large errors, which need to be sorted out, eliminate suspicious values, and improve the speed of self-adaptation. Calculate the G i value: the residual/standard deviation of each data from the mean [33].…”