Where shall we park?
Problem being addressed
While autonomous driving technologies have advanced by leaps and bounds, autonomous vehicles still face great challenges. Robust perception, prediction, and interaction in real traffic scenarios with other participants, especially human-driven vehicles, are difficult. Depending on the estimated behavior of the others, an autonomous vehicle's behaviour may be too conservative, aggressive, or in the worst case, unsafe.
Focus on the problem of predicting a human driver’s intended parking spot and future trajectory, given a set of features and levels of model abstraction. A custom simulator parking lot environment was constructed and used to generate a dataset of human parking maneuvers, including both forward and reverse parking maneuvers, as well as various parking spot selections. The researchers suggest a hierarchical model structure which can provide both intent and multi-modal trajectory predictions, and a nested model structure with a visual encoders.
Advantages of this solution
Benefits of providing intent information for trajectory prediction. Additionally, by encoding obstacles and parking lot geometry, the semantic bird’s eye view images help improve prediction performance in the long term. The hierarchical framework is capable of offering multi-modal driver behavior prediction in a relatively unstructured environment like parking lots.
Solution originally applied in these industries
Possible New Application of the Work
Lack of parking lots is becoming an urgent issues in fast growing cities; building parking lots considering the drivers' behaviour can significantly optimise their capacity, especially in case of scarce land resources and irregular usage.
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