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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.

A TGDI (turbocharged gasoline direct injection) engine is developed to realize both excellent fuel economy and high dynamic performance to guarantee fun-to-drive. In order to achieve this target, it is of great importance to develop a superior combustion system for the target engine. In this study, CFD simulation analysis, steady flow test and transparent engine test investigation are extensively conducted to ensure efficient and effective design. One dimensional thermodynamic simulation is firstly conducted to optimize controlling parameters for each representative engine operating condition, and the results serve as the input and boundary condition for the subsequent Three-dimensional CFD simulation. 3D CFD simulation is carried out to guide intake port design, which is then measured and verified on steady flow test bench.