Joakim Sorstedt

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Advanced automotive active safety systems often use sensors, such as radar and camera, to gather observations on the traffic environment around the vehicle. Through a tracking framework, these observations are refined to estimates of, e.g., position of other vehicles, pedestrians, and the road. Based on the estimates, dangerous situations can be detected(More)
Reliable and accurate vehicle motion models are of vital importance for automotive active safety systems for a number of reasons. First of all, these models are necessary in tracking algorithms that provide the safety system with information. Second, the motion model is often used by the safety application to make long-term predictions about the future(More)
This paper presents a fusion architecture designed for vehicle manufacturers that use multiple sensor systems to realize several active safety applications, ranging from standard systems to autonomous systems. Advantages and disadvantages of design choices are discussed and the methods we have chosen to implement in two demonstrator vehicles are used as(More)
An ego vehicle localization algorithm must be able to estimate where the vehicle is on the road. This is typically performed with a positioning filter that operates in global coordinates. Herein, we take a different approach, by splitting the localization problem into two parts: in-lane localization and ego lane estimation. The paper addresses the latter(More)
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