In recent years, the development of Advanced Driver-Assistance Systems (ADASs) is driving the need for more reliable and precise on-vehicle sensing. Automotive lidar and radar 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.
From a research paper in mdpi by Giuffrida, et al.
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 automotive lidar and radar is performed.
Nowadays, the most promising sensor systems to support the development of ADASs are based on radar and lidar. Both permit detection and ranging but, while the former exploits radio-frequency (RF) signals, the latter exploits optical signals. The debate on which one will be the genuinely enabling technology for autonomous driving is still open. In fact, many of the solutions that are available on the market adopt either radar, lidar or a combination of them.
For the complete paper on automotive lidar and radar CLICK HERE.
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