IEEE Signal Processing Magazine· 2020Q1· Review
Lidar for Autonomous Driving: The Principles, Challenges, and Trends for Automotive Lidar and Perception Systems
- 660citations
- Q1SCImago
- 2020year
Short summary
Automotive lidar systems, incorporating laser transmitters and beam-scanning mechanisms, are increasingly used to enhance autonomous vehicle perception by detecting vehicles and pedestrians, complementing camera and radar. This review details lidar components, compares current technologies, and outlines perception algorithms, including model-driven and deep learning approaches.
AI-generated from the title and abstract; the full text is not read.
Key points
- Lidar systems are essential for autonomous vehicle perception, complementing cameras and radar to detect surrounding entities.
- The review covers lidar system components, including laser transmitters and beam-scanning mechanisms.
- Both model-driven and deep learning approaches for lidar data processing are discussed.
- The article provides an overview of current limitations, challenges, and future trends in automotive lidar and perception.
AI-generated from the title and abstract; the full text is not read.
Abstract
Autonomous vehicles rely on their perception systems to acquire information about their immediate surroundings. It is necessary to detect the presence of other vehicles, pedestrians, and other relevant entities. Safety concerns and the need for accurate estimations have led to the introduction of lidar systems to complement camera- or radar-based perception systems. This article presents a review of state-of-the-art automotive lidar technologies and the perception algorithms used with those technologies. Lidar systems are introduced first by analyzing such a system's main components, from laser transmitter to beamscanning mechanism. The advantages/disadvantages and the current status of various solutions are introduced and compared. Then, the specific perception pipeline for lidar data processing is detailed from an autonomous vehicle perspective. The model-driven approaches and emerging deep learning (DL) solutions are reviewed. Finally, we provide an overview of the limitations, challenges, and trends for automotive lidars and perception systems.
The authors' abstract, as published at the source. IEEE Signal Processing Magazine, 2020 · DOI ↗
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Field: Instrumentation
InstrumentationPhysics and Astronomy