Читать книгу Engineering Autonomous Vehicles and Robots - Shaoshan Liu - Страница 11

The typical sensors used in autonomous driving include Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR), cameras, radar and sonar: GNSS receivers, especially those with real-time kinematic (RTK) capabilities, help autonomous vehicles localize themselves by updating global positions with at least meter-level accuracy. A high-end GNSS receiver for autonomous driving could cost well over $10 000.

LiDAR is normally used for the creation of HD maps, real-time localization, as well as obstacle avoidance. LiDAR works by bouncing a laser beam off of surfaces and measuring the reflection time to determine distance. LiDAR units suffer from two problems: first, they are extremely expensive (an autonomous driving grade LiDAR could cost over $80 000); secondly, they may not provide accurate measurements under bad weather conditions, such as heavy rain or fog.

Cameras are mostly used for object recognition and tracking tasks, such as lane detection, traffic light detection, and pedestrian detection. Existing implementations usually mount multiple cameras around the vehicle to detect, recognize, and track objects. However, an important drawback of camera sensors is that the data they provide may not be reliable under bad weather conditions and that their sheer amount creates high computational demands. Note that these cameras usually run at 60 Hz, and, when combined, can generate over 1 GB of raw data per second.

Figure 1.1 Cost breakdown of existing autonomous driving solutions.

Radar and sonar: The radar and sonar subsystems are used as the last line of defense in obstacle avoidance. The data generated by radar and sonar show the distance from the nearest object in front of the vehicle's path. Note that a major advantage of radar is that it works under all weather conditions. Sonar usually covers a range of 0–10 m whereas radar covers a range of 3–150 m. Combined, these sensors cost less than $5000.