Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system. The requirements of a frontal environment perception system cannot be satisfied by either of the existing automotive sensors. A commonly used sensor cluster for these functions consists of a mono-vision smart camera and automotive radar. The sensor fusion is intended to combine the data of these sensors to perform a robust environment perception. Multi-object tracking algorithms have a suitable software architecture for sensor data fusion. Several multi-object tracking algorithms, such as JPDAF or MHT, have good tracking performance; however, the computational requirements of these algorithms are significant according to their combinatorial complexity. The GM-PHD filter is a straightforward algorithm with favorable runtime characteristics that can track an unknown and time-varying number of objects. However, the conventional GM-PHD filter has a poor performance in object cardinality estimation. This paper proposes a method that extends the GM-PHD filter with an object birth model that relies on the sensor detections and a robust object extraction module, including Bayesian estimation of objects' existence probability to compensate for drawbacks of the conventional algorithm.

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The gradually evolving automated driving and ADAS functions require more enhanced environment perception.The key to reliable environmental perception is large amounts of data that are hard to collect. Several simulators provide realistic, raw sensor data based on physical sensor models. However, besides their high price, they also require very high computation capacity. Furthermore, most sensor suppliers provide high-level data, such as object detections, that is complicated to reproduce from simulated raw sensor data. This paper proposes a method that directly simulates the detections or object tracks provided by smart sensors. The model involves several uncertainties of the sensors, such as missed-, false detections, and measurement noise. In contrast to the conventional sensor models, this method tackles with state-dependent clutter model and considers the field of view in the detections model. The parameters of the proposed model are identified for an automotive smart radar and camera based on pre-evaluated real-world measurements. The resulting model provides synthetic object-level data with higher fidelity than the conventional probabilistic models, differing less than 2% from the precision and recall metrics of the actual sensors.