This paper provides a comprehensive survey of motion prediction and risk assessment techniques for intelligent vehicles, aiming to enhance road safety. The authors categorize these methods into three levels of abstraction: Physics-based, Maneuver-based, and Interaction-aware motion models. Physics-based models are the simplest, relying solely on physical laws, while Maneuver-based models consider driver intentions, and Interaction-aware models account for inter-vehicle dependencies. The paper discusses the trade-offs between model complexity and real-time constraints, emphasizing that the choice of risk assessment method is influenced by the selected motion model. It also reviews various techniques for trajectory prediction, including single trajectory simulation, Gaussian noise simulation, and Monte Carlo simulation. For risk assessment, the paper explores methods based on collision prediction and unexpected behavior, highlighting the importance of considering both physical collisions and deviations from expected driver behavior. The authors conclude by noting the computational challenges and the need for joint advancements in motion modeling and risk estimation to improve the reliability and real-time performance of intelligent vehicle systems.This paper provides a comprehensive survey of motion prediction and risk assessment techniques for intelligent vehicles, aiming to enhance road safety. The authors categorize these methods into three levels of abstraction: Physics-based, Maneuver-based, and Interaction-aware motion models. Physics-based models are the simplest, relying solely on physical laws, while Maneuver-based models consider driver intentions, and Interaction-aware models account for inter-vehicle dependencies. The paper discusses the trade-offs between model complexity and real-time constraints, emphasizing that the choice of risk assessment method is influenced by the selected motion model. It also reviews various techniques for trajectory prediction, including single trajectory simulation, Gaussian noise simulation, and Monte Carlo simulation. For risk assessment, the paper explores methods based on collision prediction and unexpected behavior, highlighting the importance of considering both physical collisions and deviations from expected driver behavior. The authors conclude by noting the computational challenges and the need for joint advancements in motion modeling and risk estimation to improve the reliability and real-time performance of intelligent vehicle systems.