Artificial Intelligence, particularly machine learning algorithms, is the core technology that allows autonomous vehicles to perceive their surroundings, make decisions, and control the vehicle's movement without human intervention.

Radar (Radio Detection and Ranging) sensors emit radio waves to measure the distance, speed, and direction of nearby objects, providing valuable information for autonomous vehicles to navigate safely.

Vehicle-to-Vehicle (V2V) Communication refers to the wireless exchange of information between autonomous vehicles, allowing them to share data such as speed, location, and trajectory, enhancing safety and coordination on the road.

Level 2 autonomous driving involves partial automation, where the vehicle can control certain aspects like steering, acceleration, and braking, but the driver must remain attentive and ready to take over control when needed.

Autonomous vehicles in public transportation can provide improved accessibility by offering more flexible and convenient transportation options, particularly for individuals with disabilities or those living in underserved areas.

Edge Computing brings computation and data storage closer to the autonomous vehicle, allowing for faster processing of real-time data, such as sensor inputs and traffic conditions, enabling quicker decision-making and response times.

Autonomous Vehicle Ethics encompasses the ethical considerations and guidelines specific to the development, deployment, and operation of autonomous vehicles, addressing issues such as safety, responsibility, privacy, and societal impact.

LiDAR (Light Detection and Ranging) sensors emit laser pulses to measure the distance to surrounding objects, generating a precise 3D representation of the environment, which is crucial for autonomous vehicles to navigate safely.

Unpredictable weather conditions, such as rain, snow, or fog, can significantly impact the performance of autonomous vehicles' sensors, making it challenging for them to accurately perceive and respond to their surroundings.

Machine Learning algorithms allow autonomous vehicles to learn from data, identify patterns, and make predictions, enabling them to continuously improve their performance and adapt to changing conditions.

Road Testing involves operating autonomous vehicles on public roads in real-world conditions to assess their performance, safety, and compliance with regulations.

Camera Systems equipped with advanced image processing algorithms enable autonomous vehicles to detect and classify objects such as vehicles, pedestrians, and traffic signs, providing valuable information for decision-making.

The primary goal of autonomous vehicle research and development is to enhance safety by reducing or eliminating human error, which is a major contributing factor to accidents.

Dedicated Short-Range Communications (DSRC) is a wireless technology specifically designed for vehicle-to-vehicle and vehicle-to-infrastructure communication, enabling autonomous vehicles to exchange information with each other and with traffic infrastructure.

Level 5 autonomous driving represents full autonomy, where the vehicle can handle all driving tasks in all conditions without human intervention or supervision.