## 6 Autonomous Driving
### Autonomous Vehicle Technologies
#### 6.1.2 Sensory Technology  
Advanced sensory technology is one of the most critical factors in realizing autonomous driving. In order to popularize autonomous driving, the vehicle itself is required to enhance the maneuverability of a human driver. The combination of different sensors can enable the vehicle to outperform humans in terms of visionary perception of the environment. The installation positions, coverage and maximum range of typical sensors for environment perception in an autonomous vehicle are displayed in Figure 8a,b. The existing sensory technologies for automated driving can be principally divided into six categories:  
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2. Ultrasonic sensors Sensing range: 0-2 m. Ultrasonic sensors in vehicles play an irreplaceable role in implementing automated parking, as they can detect obstacles within short distances, such as guardrails, pedestrians, vehicles, etc. [124]. However, they will be useless in medium- and high-speed situations. 3. Computer vision Sensing range: 0-120 m.  
Multi-cameras can approximate human vision, and stereo cameras can even obtain depth information about the environment. The cameras can detect colors and fonts, which makes it possible to understand the semantic information embedded in traffic signs and stoplights, a capability that other sensors do not have [125]. They can perform as a redundant system in case of failure of other sensors, thus increasing the reliability of perception and system safety. However, the perception requirements for expected autonomous driving can only be met when the visibility range is increased to 250 m [126]. Adverse weather, such as rain, fog, and low light levels, can significantly limit the perception effect of such sensors. The recognition algorithm based on such sensors has yet to be developed, with the current pedestrian recognition success rate at 95% [127]. 4. Radio detection and ranging (radar) sensors Sensing range: 0-250 m. Radio waves emitted by radar transmitters will be reflected to the receiver when encountering obstacles. The distance and relative speed between the sensor and the obstacle can be calculated through the time difference. Specifically, if short- and long-range radar sensors are installed at the periphery of an autonomous vehicle, they will monitor the real-time position and speed of surrounding objects, including vehicles and pedestrians [128]. However, 2D radar, which can scan only in the horizontal plane, cannot reconstruct the height information of obstacles, so collisions may occur when the vehicle travels on a height-limited road [129]. 3D radar sensors will be applied to solve the problem. 5. Light detection and ranging (LIDAR) sensors Sensing range: 0-200 m.  
The LIDAR sensor follows the same principle as the radar sensor but uses an invisible beam instead of radio waves as the scanning medium [130]. Combined with onboard camera data, the sensor can create the 3D point cloud around the vehicle to accurately identify obstacles, such as the presence of vehicles, pedestrians, and signage (Figure 9a). However, LIDAR is more expensive than radar due to its complex mechanical structure, difficult calibration, and high cost of laser scanning components [131]. The gradual development of solid-state LIDAR is expected to sink the price to an acceptable range. Odometry  
Wheel speed odometry uses a rotary encoder to record wheel rotation to estimate the position change relative to the initial point [132]. However, wheels are not in pure rolling motion to the ground at all times and may slide when driving on smooth roads or during sharp changes in speed. Sliding which cannot be recorded will cause cumulative errors, so the reliability of odometry will gradually decrease over time. Visual odometry attempts to analyze the frame-to-frame movement of the image to estimate the position and speed of the vehicle (Figure 9b), so it is not limited by the form of the motion to the ground [133].