Computer vision, using cameras and image processing algorithms, allows self-driving cars to perceive and understand their surroundings.

Lidar (Light Detection and Ranging) uses laser pulses to create a detailed 3D map of the surroundings, enabling long-range object detection.

Autonomy refers to the self-driving car's capability to operate independently, without requiring human input or control.

The AI system, often powered by machine learning algorithms, analyzes sensor data and makes decisions regarding acceleration, braking, and steering.

Developing algorithms capable of interpreting complex traffic scenarios and making appropriate decisions remains a significant challenge in self-driving car technology.

Sensor redundancy involves using multiple sensors of different types to enhance the accuracy and reliability of object detection, reducing the risk of accidents.

Gaining public trust and acceptance is crucial for the widespread adoption of self-driving cars, as people need to feel confident in the technology's safety and reliability.

The United States has been a leader in self-driving car development, with companies like Google, Tesla, and Uber actively testing and developing autonomous vehicle technology.

Self-driving cars have the potential to reduce traffic congestion by optimizing traffic flow, reducing accidents, and improving overall efficiency.

Lidar (Light Detection and Ranging) uses laser pulses to generate a precise 3D map of the surroundings, providing valuable information for navigation and obstacle detection.

GPS provides accurate positioning information, enabling self-driving cars to determine their location and plan optimal routes to their destinations.

Ultrasonic sensors emit high-frequency sound waves to detect nearby objects, providing short-range obstacle detection capabilities.

Adaptability refers to the self-driving car's capability to adjust its behavior and decision-making based on changing road conditions, traffic patterns, and unforeseen circumstances.

DSRC is a wireless communication technology specifically designed for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, enabling self-driving cars to exchange information and coordinate actions.

Adverse weather conditions, such as rain, snow, and fog, can impact sensor accuracy and performance, making it challenging for self-driving cars to perceive their surroundings accurately.