An autonomous car, also known as a robotic car is a vehicle that is capable of sensing its environment and moving with little or no human input. We are currently working towards building a safer driving experience through the AV’s vision system, which is capable of object and event detection and response. The vision system’s cameras constantly scan the road for moving and static objects, such as pedestrians, cyclists, other vehicles, traffic lights, construction cones, and other road features it passes along the road through a 360-degree view of its surroundings.

Then, the camera captures images of these objects, which the machine learning algorithms send to the database to determine what objects it sees. To train the system to recognize objects, it is fed millions of images of this object, for example, children of different ages, weights, heights, hair or skin coloring, wearing a variety of clothes, from different angles, etc. So if a child crosses the street, the system’s collision detection and avoidance system recognizes the body crossing the street as a child and determines that the car has to slow down or stop to allow the child to cross safely before the car can proceed.

There are different systems that help the self-driving car control the car. Systems that currently need improvement include the car navigation system, the location system, the electronic map, the map matching, the global path planning, the environment perception, the laser perception, the radar perception, the visual perception, the vehicle control, the perception of vehicle speed and direction, the vehicle control method.

The challenge for driverless car designers is to produce control systems capable of analyzing sensory data in order to provide accurate detection of other vehicles and the road ahead. Modern self-driving cars generally use Bayesian simultaneous localization and mapping (SLAM) algorithms, which fuse data from multiple sensors and an off-line map into current location estimates and map updates. Waymo has developed a variant of SLAM with detection and tracking of other moving objects (DATMO), which also handles obstacles such as cars and pedestrians. Simpler systems may use roadside real-time locating system (RTLS) technologies to aid localization. Typical sensors include Lidar, stereo vision, GPS and IMU. Control systems on automated cars may use Sensor Fusion, which is an approach that integrates information from a variety of sensors on the car to produce a more consistent, accurate, and useful view of the environment.