Abstract
Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.
Highlights
Applied Electronics Department, Faculty of Electronics, Telecommunications, and Information Technologies, Abstract: Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems
The authors propose an end-to-end seMost camera-based approaches are formulated as ego-motion forecasting, e.g., [52], quence-based network based on FlowNet [53], AtrousCNN [54] and Spatial Pyramid where the problem of ego-vehicle trajectory is solved via semantic segmentation of the data
We have reviewed current state-of-the-art sensors and deep learning methods applied to the pedestrian trajectory prediction problem
Summary
The following sensor types are often employed in the problem of PTP: radio detection and ranging (radar), light detection and ranging (LiDAR) and video camera. New techniques based on sensorial information fusion emerged. Key aspects of those technologies are briefly presented below
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