Over the past decade, the rise of Automated Driving Systems (ADS) has prompted Original Equipment Manufacturers (OEMs), regulators like NHTSA to recognize the challenges in achieving fully autonomous vehicle navigation without human assistance. Estimates suggest that a fleet of autonomous vehicles would need to drive a billion miles in various conditions to demonstrate safety and reliability—an unfeasible goal with traditional testing methods. As a result, there’s been a significant push towards creating high-fidelity simulation platforms. Sensor simulations, which replicate the outputs of vehicle-mounted sensors like cameras, radars, and LiDARs crucial for ADS and ADAS, are critical for these efforts. While initial simulations generate ideal sensor data, adding realistic imperfections is necessary to mimic real-world data accurately. Different sensors come with their own challenges, such as sensor noise, biases, and faults, that must be accounted for in simulations. Validation of these simulations involves comparing them with real-world data to ensure similarity in output. Understanding the functional dependencies between sensor data generation and perception system performance is essential, particularly when training AI models with both simulated and real data.
