From an article in Electronic Design by Wayne Lyons.
The autonomous-driving landscape is evolving at a rapid pace. The number of highly automated vehicles shipping each year is set to grow at a CAGR of 41% between 2024 and 2030. This rapid growth has led to unprecedented demand from automotive brands for precise and reliable sensor technology as they seek to deliver accurate, trusted, and, ultimately, fully autonomous driving.
In pursuit of this goal, LiDAR (light detection and ranging) sensors have become indispensable to auto manufacturers and automotive equipment suppliers. They can “read the road” by enabling depth perception and range detection with sufficient resolution for object classification.
Yet, as we move into the next generation of autonomous-driving solutions—from the latest innovations in active safety systems to driverless vehicles—the capabilities of edge systems like LiDAR must be expanded so that they can offer higher depth resolution and reliability to overcome increasingly more complex scenarios.
Incorporating adaptive-computing technologies like FPGAs and adaptive SoCs enables companies to achieve the end goal of a comprehensive perception platform. Such a platform would navigate complicated driving environments and identify potential hazards with exceptional precision.
Types of LiDAR System Architectures
When examining LiDAR systems, the three primary categories of architectures are mechanical (non-solid), MEMS (semi-solid), and flash-based (solid-state). Each has advantages and disadvantages based on the application use case.
Mechanical systems are the most widely deployed systems (Table 1). These systems use a rotating emitter to send a light wave, which bounces off an object and is sent back to a receiver. The emitters rotate extremely fast to achieve a 360-degree field-of-view, otherwise known as a point cloud. These systems have the advantage of a long range and wide field-of-view, but the downsides are that they’re larger and costly.
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