From Silicon Valley in the USA to bustling Beijing in China, self-driving cars, especially the ones driven without the assistance of a driver, are popping up everywhere and are considered to be the next big thing in the automobile industry. These self-driving cars, also known as autonomous vehicles (AVs), use a combination of advanced sensors, artificial intelligence (AI), and high-performance computing to navigate roads without human intervention. They rely on multiple technologies, including machine learning, computer vision, and real-time data processing, to perceive their surroundings, make decisions, and drive safely.
The automobile industry currently classifies 6 levels of driving automation ranging from Level 0 (fully manual) to Level 5 (fully autonomous). While the term self-driving is often used interchangeably with autonomous, there is a big difference between the two. A self-driving car can drive itself in some or even all situations, but a human passenger must always be present and ready to take control. Self-driving cars would fall under Level 3 (conditional driving automation) or Level 4 (high driving automation). They are subject to geofencing, unlike a fully autonomous Level 5 car that could go anywhere.
Autonomous cars rely on an array of sensors to build and update a real-time map of their surroundings. Radar sensors track nearby vehicles, while cameras identify traffic signals, interpret road signs, and monitor pedestrians and other cars. Lidar (Light Detection and Ranging) technology uses laser pulses to measure distances, detect lane markings, and recognize road boundaries. The Lidar creates high resolution 3D maps of the environment, helping the car detect obstacles, pedestrians, and other vehicles. Additionally, ultrasonic sensors near the wheels help with close-range tasks like parking by sensing curbs and adjacent vehicles. Then there are multiple cameras fitted in the car which provides visual data for lane detection, traffic signs, and pedestrian recognition. These cars also have GPS and IMU (Inertial Measurement Unit). GPS provides location data, while IMU tracks acceleration and rotation. This helps the car in navigation and maintaining the vehicle’s position on the map.
All this sensor data is processed by highly capable software, which determines the optimal route and controls the car’s acceleration, braking, and steering. The system combines predefined traffic rules, obstacle detection algorithms, predictive models, and object recognition to ensure safe and efficient navigation.
Waymo, formerly known as the Google Self-Driving Car Project, explains that before Waymo Driver, its fully autonomous driving technology, begins operating in a new area, it first maps the territory with incredible detail, from lane markers to stop signs to curbs and crosswalks. Then, instead of relying solely on external data such as GPS which can lose signal strength, the Waymo Driver uses these highly detailed custom maps, matched with real-time sensor data and artificial intelligence (AI) to determine its exact road location at all times.
Driving situations can involve hundreds of objects, each with their own unique behaviors and intentions. The Waymo Driver takes the information it gathers in real time, as well as the experience it has built up over its 20+ million miles of real-world driving and 20+ billion miles in simulation, and leverages AI to anticipate what other road users might do. Waymo said that it understands how a car moves differently than a cyclist, pedestrian, or other object, and then predicts the many possible paths that the other road users may take, all in the blink of an eye.