From its use in metrology and topography, lidar (light detection and ranging) technology has come a long way to quickly become one of the key technologies in autonomous vehicle (AV) advancements.

As automakers progress with real-world testing, it’s clear that next-generation sensors offer intriguing features but differ from the silver bullet many thought they’d be at first.

Without driver/human inputs, AVs require sensors and computers to work together to read the road and surrounding environment. Most advanced driver aids in the world today use a combination of radar (microwave) and sonar (sound) to warn about unseen threats and help stop a vehicle before a collision occurs.

Lidar can perform similar functions, developing an image of the environment around it while still representing the best option for AVs to see the light.

Lidar Technology in Automotive

The automotive industry is undergoing an existential transformation with autonomous driving (AV) and advanced driver assistance systems (ADAS). To achieve a fully autonomous driving experience, sensor fusion is positioning itself as the key enabler in the automotive industry. While sensor fusion focuses on software algorithms combining the data collected from cameras, radar, and lidar to obtain meaningful decision-making information, data quality is paramount. While there’s still a long way to go in mimicking the human senses and brain for level 5 driving automation, sensor fusion is a further step in that direction.

Cameras and automotive radar have been the pillars for up to level 2 driving automation. Level 3 and higher require additional sensors and redundancy. For example, car manufacturers use radar in various systems – blind-spot monitoring systems to detect vehicles before lane change, automatic emergency braking systems to stop a vehicle before it touches an obstacle, and adaptive cruise control to maintain a consistent distance between two cars.

Automotive lidar brings up high resolution and accuracy, low light effectiveness, and 3D mapping to the other two sensors. Lidar tracks obstacles and vehicles to maintain safe distances; it helps identify road signs, traffic signals, and road markings for real-time hazard analysis, ensuring autonomous vehicles’ effective operation. However, automotive lidar is yet to go mainstream due to its cost.

Lidar 101

Lidar (light detection and ranging) is a device that uses laser pulses for detection and ranging; it can operate at different wavelengths like 850nm, 905nm, 940nm, and 1550nm. The lidar sensor comprises a laser source acting as a transmitter, a photodetector acting as a receiver, and an assembly of lenses or optics to steer and collect the laser pulses. When the pulse touches an object, it bounces back to the lidar unit. The system then receives the pulse and calculates the distance with the object based on the time elapsed between the emission of the pulse and the reception of the return beam. As the beams return to the system, they begin forming a picture of the vehicle’s surroundings and use computer algorithms to piece together shapes for cars, people, and other obstacles. Lidar’s use of pulsed lasers allows it to map the 3D model of an environment quickly and more accurately than radar or sonar.

There are two types of lidar:

* Time of Flight (ToF) lidar: uses the time difference between transmitted and reflected laser pulse to calculate the target’s distance, also called range.

* Frequency Modulated Continuous Wave (FMCW) lidar: uses frequency difference between transmitted and received modulated laser chirp to calculate distance and derive the target’s velocity. FMCW lidar uses coherent detection that helps achieve improved range resolution and the ability to measure dim and bright targets simultaneously.

We can also categorize lidars based on detection or scanning technology.

As with radar-based systems, lidar sensor makers must ensure their systems quickly and reliably detect objects, enabling advanced driver assistance systems (ADAS) to work correctly before being commercially deployed. To properly test sensors, designers must often depend upon large floor spaces and traditional target boards for range and reflectivity tests. The industry also faces challenges in reducing sensor costs and scaling to mass production.

The main challenge for the mass adoption of lidar by automotive OEMs is bringing down the cost of lidar sensors and possibly making them comparable to radar. The price of lidar devices has decreased significantly in the last few years; however, there’s a scope to make it more competitive. Many factors are driving the cost up, such as R&D, material and production cost, and lower production volumes.

Going forward

Automotive lidar is a promising step toward a fully autonomous driving experience. With sensor fusion and more precise algorithms, there will be higher redundancy of meaningful data streams. Data that one day will help reconstruct a decision-making process close to the level of complexity that a human brain can handle.

By: DocMemory
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