Self-driving minibuses are seen by many as a possible solution for ensuring rural mobility and keeping people in the countryside connected in an environmentally friendly way – thus enabling rural residents to be part of the transition to a carbon-neutral economy. However, we need to make a realistic assessment of the true extent to which the AI built into these vehicles, and the resources they consume, are actually beneficial to the environment.
Object detection and classification, scene understanding, localization and other functions rely on cameras. Typically, image sensors capture the environment, producing data that is then processed by computer vision algorithms.
These sensors use high-frequency sound waves to detect objects and determine how close they are. They are usually inexpensive and compact, and are used for parking assistance, object detection and obstacle avoidance.
This sensor uses radio waves to detect objects and their distance in addition to measuring speeds and angles. The radar function is reliable even in poor weather conditions. It can be used for obstacle detection and tracking, lane detection or vehicle tracking.
Remote sensing technology uses laser light for obstacle detection and navigation to quickly and accurately measure distances and create a high-resolution 3D map of the environment.
The satellite-based navigation system provides accurate location and time information. In autonomous vehicles, it enables global localization for navigation and mapping.
IMUs are sensors that measure acceleration and angular velocity. They are used in self-driving vehicles to detect and control movement.
These sensors measure the rotational position of the wheels to provide information about the vehicle’s movement and position.