Andreas Alin

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Many automotive systems use linear approaches to track and predict other traffic participants. While this may be appropriate on highways, linear predictions do not work properly on curved roads or lane crossings. This contribution introduces a generic way for including environmental knowledge - such as the lane trajectory ahead - to anticipate yaw rate and(More)
Neuroscientific research suggests that the human brain encodes spatial information in a Bayesian-optimal way by means of distributed, neural population codes. In this paper we apply this concept to Advanced Driver Assistance Systems, introducing a grid-based population code for tracking and predicting the behavior of individual vehicles. The representation(More)
Estimating and tracking the positions of other vehicles in the environment is important for advanced driver assistant systems (ADAS) and even more so for autonomous driving vehicles. For example, evasive strategies or warnings need accurate and reliable information about the positions and movement directions of the observed traffic participants. Although(More)
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