Synthetic Scenario Generation from Real Road Data for Indian Specific ADAS Function Verification and Validation

Abstract

<div class="section abstract"><div class="htmlview paragraph">Advanced Driver Assistance Systems (ADAS) play a crucial role in enhancing road safety by providing intelligent assistance to drivers. To ensure the reliability and effectiveness of ADAS functions, rigorous verification and validation processes are necessary. One critical aspect of this process is scenario generation, which involves creating diverse and representative driving scenarios for testing and evaluating ADAS functions.</div><div class="htmlview paragraph">This paper proposes a novel approach for synthetic scenario generation specifically tailored for Indian road conditions. The approach leverages real-time road data collected from various sources, including camera sensors, Lidar sensor, GPS devices, and traffic monitoring systems. The collected data is processed and analyzed to extract relevant information, such as road geometries, traffic patterns, and environmental conditions.</div><div class="htmlview paragraph">Based on the extracted data, a synthetic scenario generation algorithm is developed, which takes into account the unique characteristics of Indian roads, including complex traffic scenarios, diverse road conditions, and challenging driving situations. The algorithm incorporates statistical models and machine learning techniques to generate realistic and diverse scenarios that mimic real-world driving conditions.</div><div class="htmlview paragraph">The synthetic scenarios generated by the proposed approach are used for the verification and validation of ADAS functions specific to the Indian context. The scenarios cover a wide range of critical scenarios, including lane changes, pedestrian crossings, intersection scenarios, and adverse weather conditions. By using synthetic scenarios, the testing process becomes more efficient and cost-effective, as it reduces the reliance on physical testing and enables comprehensive coverage of various challenging scenarios.</div><div class="htmlview paragraph">The effectiveness of the synthetic scenario generation approach is evaluated through extensive simulations and real-world testing. The results demonstrate that the generated scenarios effectively capture the intricacies of Indian road conditions and provide valuable insights into the performance and robustness of ADAS functions. Furthermore, the approach can be customized and adapted for other regional contexts, making it a versatile tool for ADAS verification and validation.</div></div>