Control and Actuation

The controller is responsible for taking the desired trajectory and adjusting the vehicle's actuators (such as steering, throttle, and brakes) to achieve that trajectory.

MPC controllers are widely used in autonomous vehicles for lateral control due to their ability to handle complex system dynamics and constraints.

Actuators are responsible for applying physical force to the vehicle's control surfaces (such as steering, throttle, and brakes) to achieve the desired motion.

Electric actuators are widely used in autonomous vehicles for steering control due to their fast response time, high precision, and energy efficiency.

Sensors are responsible for gathering information about the vehicle's surroundings, such as the position of other vehicles, pedestrians, and obstacles.

Lidar sensors are widely used in autonomous vehicles for detecting obstacles due to their ability to provide accurate and detailed 3D representations of the surrounding environment.

Decision-making algorithms are responsible for processing sensor data and making decisions about the vehicle's trajectory, such as lane changes, overtaking, and emergency maneuvers.

Hybrid algorithms, which combine rule-based and machine learning techniques, are widely used in autonomous vehicles for path planning due to their ability to handle complex scenarios and adapt to changing environments.

Communication systems are responsible for enabling autonomous vehicles to communicate with each other and with roadside infrastructure, such as traffic lights and road signs.

DSRC is a dedicated wireless communication technology specifically designed for V2V communication in autonomous vehicles due to its low latency and high reliability.

Localization systems are responsible for determining the vehicle's position and orientation in the environment using various sensors, such as GPS, IMU, and odometry.

RTK GPS is a high-precision localization technique that uses corrections from a nearby reference station to improve the accuracy of GPS positioning.

Mapping systems are responsible for creating and maintaining a map of the environment using various sensors, such as lidar, camera, and radar.

LiDAR-based mapping is widely used in autonomous vehicles for creating high-definition maps due to its ability to generate accurate and detailed 3D representations of the environment.

Simulation systems are used to test and validate autonomous vehicle systems in a virtual environment, allowing engineers to evaluate the performance and safety of the system before deploying it in real-world scenarios.