The ability of a vehicle to navigate and operate without human intervention, utilizing AI and sensor technologies.

A field of AI that enables machines to interpret and understand visual information from the world, crucial for object detection.

A type of machine learning where agents learn to make decisions by receiving rewards or penalties from their actions.

The imitation of real-world processes or systems over time, used to test and validate algorithms in controlled environments.

The study of moral principles guiding the development and deployment of AI technologies, especially in autonomous systems.

The process of integrating data from multiple sensors to improve the accuracy of perception in autonomous vehicles.

A remote sensing technology that measures distance by illuminating a target with laser light, essential for mapping environments.

The computer vision task of identifying and locating objects within an image or video stream.

An open-source simulator for autonomous driving research, providing realistic environments for testing algorithms.

Robot Operating System, a flexible framework for writing robot software, widely used in autonomous vehicle development.

The capability of processing data and providing output almost instantaneously, essential for safe navigation.

The process of determining a vehicle's position and planning a route to a destination, crucial for autonomous driving.

A collection of data used to train machine learning models, critical for developing effective computer vision algorithms.

The assessment of an algorithm's effectiveness based on defined metrics, ensuring it meets industry standards.

The process by which an autonomous system chooses actions based on input data and learned experiences.

Structured approaches to evaluate the functionality, reliability, and safety of autonomous driving simulations.

In reinforcement learning, an entity that makes decisions and learns from the environment to achieve goals.

Mechanisms used in reinforcement learning to provide feedback to agents, guiding their learning process.

Guidelines that help evaluate the moral implications of AI technologies in real-world applications.

Detailed examinations of specific instances or examples, often used to illustrate ethical dilemmas in AI.

The comprehensive records of processes, implementations, and evaluations, critical for transparency and reproducibility.

Collaborative dialogues among students to explore ideas, challenges, and ethical considerations in autonomous driving.

The field focused on the design, development, and manufacturing of vehicles, integral to autonomous driving technology.

Businesses involved in the development and application of technology solutions, including autonomous vehicles.

Organizations dedicated to advancing knowledge and technology, often collaborating on autonomous driving projects.

Regulations and criteria established to ensure the safe operation of autonomous vehicles in real-world scenarios.