Lidar (Light Detection and Ranging) is a commonly used sensor for obstacle detection in autonomous vehicles. It emits laser pulses and measures the time it takes for the pulses to reflect back from objects, creating a detailed 3D map of the surroundings.

Cameras in autonomous vehicles are primarily used for object recognition. They capture images of the surroundings and use computer vision algorithms to identify and classify objects such as vehicles, pedestrians, and traffic signs.

Radar (Radio Detection and Ranging) is commonly used to measure the distance between an autonomous vehicle and other objects. It emits radio waves and measures the time it takes for the waves to reflect back, providing information about the distance and relative velocity of nearby objects.

Using multiple sensors in autonomous vehicles provides redundancy and reliability, increases the field of view, and improves the accuracy and precision of perception. By combining data from different sensors, autonomous vehicles can create a more comprehensive and accurate understanding of their surroundings.

Perception is the process of interpreting sensor data to understand the environment around an autonomous vehicle. This involves tasks such as object detection, classification, and tracking, as well as estimating the position and orientation of the vehicle.

Cameras are commonly used for lane detection in autonomous vehicles. They capture images of the road and use computer vision algorithms to identify lane markings and boundaries.

Ultrasonic sensors are commonly used to detect objects at close range in autonomous vehicles. They emit ultrasonic waves and measure the time it takes for the waves to reflect back, providing information about the presence and proximity of nearby objects.

GPS (Global Positioning System) is commonly used for localization and mapping in autonomous vehicles. It provides information about the vehicle's position, velocity, and orientation, allowing the vehicle to determine its location and navigate through the environment.

Mapping is the process of creating a map of the environment around an autonomous vehicle. This involves collecting data from sensors and using algorithms to build a representation of the environment, including roads, lanes, landmarks, and other objects.

Cameras are commonly used for traffic sign recognition in autonomous vehicles. They capture images of traffic signs and use computer vision algorithms to identify and classify the signs, extracting information such as speed limits, stop signs, and yield signs.

Planning is the process of planning a safe and efficient path for an autonomous vehicle to follow. This involves taking into account the vehicle's current state, the environment, and traffic regulations to generate a trajectory that avoids obstacles, adheres to speed limits, and follows traffic rules.

IMU (Inertial Measurement Unit) is commonly used for measuring the vehicle's orientation and angular velocity. It contains sensors such as accelerometers and gyroscopes that provide information about the vehicle's linear acceleration and angular velocity, allowing the vehicle to determine its orientation and motion.

Localization is the process of estimating the position and orientation of an autonomous vehicle. This involves using sensor data, such as GPS and IMU data, to determine the vehicle's location and orientation in the environment.

Cameras are commonly used for detecting pedestrians and cyclists in autonomous vehicles. They capture images of the surroundings and use computer vision algorithms to identify and classify objects, including pedestrians and cyclists.

Sensor fusion, data integration, and information aggregation are all terms used to describe the process of combining data from multiple sensors to create a more comprehensive understanding of the environment. This involves combining data from different sensors, such as cameras, radars, and lidars, to create a more accurate and reliable representation of the surroundings.