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V2X is a wireless communication technology that enables data exchange between a vehicle and its environment, such as other vehicles, pedestrian devices, infrastructure, and the traffic management system.

Fail-safe systems are designed to ensure that, in the event of a malfunction or system failure, the autonomous vehicle will default to a safe state, either by stopping or handing control back to the human driver.

AI and deep learning algorithms interpret sensor data, make decisions, predict outcomes, and learn from experience to improve the vehicle's safety and operation over time.

Ultrasonic sensors use sound waves to detect nearby objects, particularly useful for parking assistance and close-range detection around the vehicle.

IMUs measure and report a vehicle's specific force, angular rate, and sometimes the magnetic field surrounding the vehicle, which is used to calculate its attitude, velocity, and position.

Machine vision utilizes cameras and image processing algorithms to interpret the visual world, identify objects, read traffic signs, and provide input for vehicle navigation systems.

GPS receivers provide vehicles with precise geolocation information, used in conjunction with maps for route planning and navigation guidance.

Path planning algorithms help autonomous vehicles determine the most efficient and safe route from point A to B, considering static and dynamic environmental data.

LIDAR uses pulsed laser light to measure distances to surrounding objects. It creates high-resolution 3D maps that autonomous vehicles use for navigation and obstacle detection.

Adaptive Cruise Control (ACC) automatically adjusts the vehicle's speed to maintain a safe distance from the vehicle ahead, using RADAR and sometimes LIDAR sensors.

Decision making systems in autonomous vehicles analyze real-time data to make critical driving decisions, such as when to change lanes, turn, or brake, often based on machine learning algorithms.

Over-the-air software updates allow for the wireless transmission and installation of new software versions, security patches, and system improvements, ensuring the autonomous vehicle stays up-to-date with the latest features and fixes.

Sensor fusion is the process of integrating and interpreting data from multiple sensors to produce a more robust and accurate understanding of the vehicle's environment.

RADAR employs radio waves to determine the distance and speed of objects. It is robust to weather conditions and is used alongside LIDAR for redundancy and improved detection.