What Is Sensor Fusion?
Levels of Sensor Fusion
Sensor Fusion for Automotive
Sensor fusion is employed in several areas of automotive services. Even a GPS navigation system can blend data from other sensors alongside GPS satellite information, such as the inertial measurement unit (IMU), which detects specific force, angular rate, and orientation. The Advanced Driver-Assistance System (ADAS) will be the most ubiquitous use of sensor fusion. A front radar will most commonly be employed for detecting the distance to the car in front for adaptive cruise control, but LiDAR and cameras might also augment this. Cameras may also add lane-keeping assistance to ADAS and adaptive cruise control. Ultrasonic sensors will also be employed to assist low-speed emergency braking.
Beyond ADAS, the emerging domain of autonomous driving systems is a key area where sensor fusion will be essential. To deliver safe self-driving cars, they must combine inputs from radar, cameras, LiDAR, and GPS to recognize and navigate through surroundings. These systems will employ self-driving algorithms that have evolved using immense amounts of data collected from many hours of live testing combined with live information at the edge from a vehicle as it drives. New sensor data will continue to be sent back to the cloud to be fused with historical data, AI/ML Level 4 improvements and Level 5 user inputs. So autonomous driving systems will constantly improve with use, just as human drivers gain experience.
Benefits of Sensor Fusion
Different types of sensors have a variety of strengths and weaknesses. Radar is good in all kinds of weather but can be slower than other systems and has trouble resolving nearby objects from each other. LiDAR is excellent for detecting objects in three dimensions and has accurate range but isn’t tolerant of heavy rain, snow, or fog. Cameras can classify objects, detect angular position, and provide a holistic view of the scene but are also susceptible to weather, lighting levels and dirt on the lens. Ultrasonic sensors are cheap but have a very short range.
Combining inputs from multiple sensors of the same type can improve accuracy. Combining multiple types can take advantage of where each is strong and negate their weaknesses. A sophisticated algorithm can use the data from each sensor to prevent false detections and avoid missing important objects. This is crucial in automotive safety. If an automatic braking system fails to spot a pedestrian or cyclist, this can lead to a fatal accident. Adaptive cruise control that fails in poor weather will be too dangerous. Sensor fusion has a vital role in improving vehicle safety.
Interior and Exterior Sensor Fusion
Most of the focus of sensor fusion has been on exterior sensors such as radar, cameras, LiDAR, and ultrasonics. But automobiles are now taking data from sensors inside the car to make even more sophisticated systems. These include sensors on the steering wheel to detect whether a driver has their hands on the wheel when using a Level 2 autonomous system, which requires this. A cabin camera can detect if a driver has their eyes on the road or is drowsy.
Combining interior and exterior sensors can add another layer of safety precautions. For example, if the driver is inattentive to the road, the external systems could become more sensitive and immediately react to threats. Or it can provide a warning if you are looking down at the infotainment screen and something outside the car needs attention, such as a car changing lanes in front unexpectedly. Or, if you’re stationary and about to make a turn, interior and exterior sensor fusion could warn you of a pedestrian or cyclist appearing in the direction you are not looking. The result will be even safer driving, thanks to sensor fusion.
Check Out Our Other Ultimate Guides
Embedded Systems Security