Advanced Certificate in Vehicle Automation Simulation: Realistic Scenarios
-- ViewingNowThe Advanced Certificate in Vehicle Automation Simulation: Realistic Scenarios course is a comprehensive program designed to equip learners with the essential skills needed to excel in the rapidly evolving field of vehicle automation. This course is crucial in today's industry, where autonomous vehicles are becoming increasingly popular, and there is a high demand for professionals who can design and simulate realistic scenarios for vehicle automation.
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• Advanced Vehicle Dynamics Simulation: This unit will cover the principles of vehicle dynamics and how to simulate them in a virtual environment. Students will learn about tire forces, suspension kinematics and compliance, and vehicle stability control systems. • Realistic Traffic Scenario Generation: This unit will teach students how to create realistic traffic scenarios for simulation. Topics covered will include traffic flow theory, vehicle following models, and pedestrian dynamics. • Sensor Simulation for Autonomous Vehicles: In this unit, students will learn about the different sensors used in autonomous vehicles, such as cameras, lidar, and radar, and how to simulate their behavior in a virtual environment. • Autonomous Vehicle Decision Making: This unit will cover the decision-making algorithms used in autonomous vehicles, such as rule-based systems, behavior-based systems, and machine learning algorithms. Students will learn how to implement these algorithms in a simulation environment. • Validation and Verification of Simulation Models: This unit will teach students how to validate and verify the accuracy of their simulation models. Topics covered will include statistical analysis, sensitivity analysis, and model calibration. • High-Performance Computing for Vehicle Simulation: This unit will cover the principles of high-performance computing and how they can be applied to vehicle simulation. Topics covered will include parallel computing, distributed computing, and cloud computing. • Machine Learning for Vehicle Simulation: In this unit, students will learn about the application of machine learning techniques in vehicle simulation. Topics covered will include supervised learning, unsupervised learning, and reinforcement learning. • Simulation Visualization and Analysis: This unit will teach students how to visualize and analyze the results of their simulation experiments. Topics covered will include data visualization techniques, statistical analysis, and data mining.
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- ThreeFourHoursPerWeek
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