A Digital Twin Approach for Perception Development and Validation in Autonomous Vehicles*

The development of autonomous vehicles (AV) and advanced driver assistance systems (ADAS) relies on effective perception systems to interpret complex environmental scenarios through a robust combination of sensors. Traditional road testing is limited in fully exploring the operational design domain, especially in rare or hazardous conditions. This study investigates the viability of sensor simulation as a complementary evaluation method, focusing on lidar technology. A “digital twin” is an accurate simulation model replicating real-world sensor interactions and behaviors. Through detailed analysis of synthetic lidar simulations and real-world drive logs, we find that digital twins can effectively replicate real-world scenarios, albeit with some variance. This paper discusses the technical challenges and limitations in achieving high-fidelity simulations and the potential of these technologies to improve the safety, reliability, and efficiency of autonomous systems. Unlike previous approaches, our pipeline provides an end-to-end solution from drive data collection to sensor simulation, supporting both object-level and sensor-level validation with reduced data input requirements.