India’s self-driving technology ecosystem is thriving and innovation is happening at all levels, including sensor technology.
The Consumer Electronic Show (CES) is an influential technical event held every January. In this year’s edition (CES 2022), General Motors announced plans to introduce personalized self-driving cars by 2025. Mobileye, a leader in autonomous driving, has released a technology roadmap focused on robustness and safety. These companies are not the only ones. Waymo has been driving an unmanned vehicle in Phoenix since 2019. Apple is reported to have plans to build self-driving cars in the next few years. At the heart of this technology are three sensors: camera, radar, and lidar (light detection and distance measurement), all of which help the vehicle to accurately recognize its surroundings. Surprisingly, much of this sensor technology is already present in cars on the road today. Cameras and radar sensors regularly provide driver assistance features such as keeping the vehicle within the lane markings, warning that the vehicle is approaching while changing lanes, and maintaining a safe distance to the vehicle in front. Provide to.
Self-driving technology is advancing rapidly, with several large companies investing heavily. This technology basically relies on three sensors: camera, radar and lidar. The camera can identify colors, shapes, traffic signs, and more. However, it does not send a detection signal and depends on the ambient light reflected from the object. Radar can send its own signal, but it cannot identify colors, recognize road signs, and has low spatial resolution. LIDAR scans the environment with a laser beam. In many respects, it combines the best of both radar and camera features, but it is unable to transmit fog or distinguish colors. Given the market potential, great efforts have been made to reduce costs and address the performance gaps in these sensors. 3 sensors
The camera system behaves like the human eye, distinguishing colors, shapes, and recognizing traffic signs, lane signs, and more. Most cars have a stereo camera. That is, the two cameras are separated by a short distance. This allows you to perceive depth (like a human). However, there are restrictions on the camera. It does not send a sensing signal and depends on the ambient light reflected from the object. Therefore, in the absence of adequate ambient light (at night), it limits its ability, as well as other environmental conditions such as fog and dazzling sunlight.
Radar sensors send their own signal. The signal hits the target, bounces off, and reflects off the radar. Therefore, unlike cameras, radar does not depend on ambient light. In addition, radar sends radio waves that can pass through fog. Radar measures the time from the transmission of a signal to the arrival of a reflected signal from the target to estimate the distance to the target. The moving target causes a frequency shift (Doppler shift) in the signal, allowing the radar to measure the target speed instantly and accurately. Therefore, radar can accurately measure the range and speed of a target with little reliance on environmental conditions such as fog, rain, and bright sunlight. However, unlike cameras, radar cannot identify colors or recognize road signs. The spatial resolution of the radar is also low. As a result, approaching vehicles are displayed as blobs, and individual features (wheels, body contours, etc.) cannot be identified as with cameras. In this way, the functions of the camera and radar sensor complement each other, so many cars are equipped with both a camera and a radar.
LIDAR is another sensor used in self-driving cars. LIDAR scans the environment with a laser beam. In many respects, LIDAR combines the best of both radar and camera features. Similar to radar, you can accurately determine the distance by generating your own transmit signal (and therefore independent of daylight) and measuring the time difference between the transmit signal and the reflected signal. The narrow laser beam used for sensing guarantees the same spatial resolution as a camera. However, LIDAR has its drawbacks. Lidar signals cannot penetrate fog, identify colors, or read traffic signs. This technology costs significantly more than radar and cameras.
Given the market potential, much effort has been made to both reduce costs and address the performance gaps in each of these sensors. Radar companies are developing imaging radars that significantly improve the spatial resolution of radars, but new technologies that can reduce the cost of lidar are being considered. At the same time, camera-based visual recognition capabilities continue to be enhanced by deep learning applications. However, each sensor has limitations based on physics and technology. Only the camera can recognize traffic signs, but it cannot match the performance of radar in bad weather. Similarly, radar cannot match the spatial resolution of the camera or LIDAR. Experts agree that self-driving car technology cannot be based on a single type of sensor. However, there is some debate about the optimal sensor suite that is safe and cost effective. Some researchers believe that cameras and radar with excellent deep learning backends will eliminate the need for lidar.
Indian sensor technology
India’s self-driving technology ecosystem is thriving and innovation is happening at all levels, including sensor technology. Most of the research and development of Texas Instruments Automotive Radar is done at the Development Center in India. Velodyne, a pioneer in LIDAR technology, recently opened a development center in Bangalore. Steredian Semiconductors, an Indian-based startup, has developed an imaging radar solution. Many of the major semiconductor companies (NXP, TI, Qualcomm) are developing hardware and software for perceptual algorithms that feed these sensors at R & D centers in India.
Sandeep Rao belongs to Texas Instruments
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