Abstract
Trajectory prediction is a fundamental and challenging task for numerous applications, such as autonomous driving and intelligent robots. Current works typically treat pedestrian trajectories as a series of 2D point coordinates. However, in real scenarios, the trajectory often exhibits randomness, and has its own probability distribution. Inspired by this observation and other movement characteristics of pedestrians, we propose a simple and intuitive movement description called a trajectory distribution, which maps the coordinates of the pedestrian trajectory to a 2D Gaussian distribution in space. Based on this novel description, we develop a new trajectory prediction method, which we call the social probability method. The method combines trajectory distributions and powerful convolutional recurrent neural networks. Both the input and output of our method are trajectory distributions, which provide the recurrent neural network with sufficient spatial and random information about moving pedestrians. Furthermore, the social probability method extracts spatio-temporal features directly from the new movement description to generate robust and accurate predictions. Experiments on public benchmark datasets show the effectiveness of the proposed method.
Highlights
A pedestrian’s trajectory is multimodal, and closely depends on the person’s hearing, vision, touch, thoughts, and personality, and is affected by other factors such as the static environment, dynamic human–human interactions, and planned destinations
In order to solve the above problems, we propose the concept of a trajectory distribution, which is an intuitive and effective motion description
Based on the proposed trajectory distribution, we further develop a new method called the social probability method for predicting robust and accurate pedestrian trajectories
Summary
A pedestrian’s trajectory is multimodal, and closely depends on the person’s hearing, vision, touch, thoughts, and personality, and is affected by other factors such as the static environment, dynamic human–human interactions, and planned destinations. The purpose of trajectory prediction is to enable machines, such as robots, self-driving cars, and intelligent tracking systems, to have the ability to predict future trajectories based on historical trajectories. This is a fundamental but extremely challenging task. The methods above can predict multi-future trajectories, their inputs are still fixed points, and take a trajectory as a two-dimensional sequence of coordinates (xt, yt). Each coordinate is fixed, and these separate points fail to represent randomness of the trajectory. These methods cannot demonstrate uncertainty in the trajectory caused by inherent randomness
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
