A Motion Logic Network for Pedestrian Motion Prediction

Accurate and fast motion prediction such as pedestrian motion prediction (PMP) is crucial for safe autonomous driving. Much research effort has been devoted to studying the reactive behaviors of pedestrians, such as the interaction between pedestrians or the interaction between pedestrians and the environment. However, compared with behavioral logic, the current motion state of pedestrians has a greater influence on the future trajectory. In this work, we propose a motion logic network (MLN) to improve both the accuracy and efficiency of pedestrian motion prediction. Compared with the traditional data-driven neural networks, the concept of motion logic is directly introduced into the network so that the training of the network is not required. In particular, instead of fitting the network based on inputs and target outputs, the proposed method directly adopts motion logic to predict the future trajectory. To illustrate the performance of the proposed MLN, several experiments have been performed. For acceleration and deceleration motion in practical experiment, the average displacement error (ADE) of MLN has an improvement of as high as 6.25% than CVM's, while the final displacement error (FDE) of MLN has an improvement of 11.1%. In terms of efficiency, MLN is a hundred times faster than LSTM. It indicates that motion logic plays an important role in the development of prediction algorithms for pedestrian motion. Note to Practitioners-This article is motivated by the effects of pedestrians' different motion states on the future trajectory. For example, the pedestrians' state may also change drastically in some situations such as dashing across the road or stopping in anticipation of a bicycle crossing the path. This work explores the use of pedestrians' physical motion states to develop a network that emphasizes the logical features of pedestrian motion so as to directly estimate the future motion trajectory and trend based on the motion logic. For the first time, the concept of motion logic and the resultant training-free motion logic network (MLN) is proposed, which takes into account the accuracy and efficiency performance of the algorithm. Compared with the state-of-the-art pedestrian prediction methods, the proposed method can better predict the trajectory of pedestrians in more complex motion states. Moreover, a series of simulations and practical experiments with pseudo-constant velocity motion and acceleration/deceleration motion was taken to verify the performance of the proposed MLN.