Technical Paper
Data-Driven Confidence Model for ADAS Object Detection
2020-04-14
2020-01-0695
The majority of road accident is due to human error. Advanced Driver Assistance System (ADAS) has the potential to reduce human error and improve driving safety. Customers have shown a growing acceptance for ADAS technology. With the rising demand for safety and comfortable driving experience, the global market for ADAS is expected to grow to $67 billion by 2025. A reliable ADAS system requires an accurate and robust object-detection system. There is often a trade-off in tuning the system. On one hand, miss-detection can cause accidents; on the other hand, false-detection can result in ghost-braking and harm the driving experience. The ADAS system can access various information from different sources. However, a unified confidence model, which combines different indicators, has not been much studied in the literature. In this paper, we propose a data-driven method, which utilizes the features from radar, camera and the tracking system to produce a high-level confidence model.