Probabilistic trajectory prediction of heterogeneous traffic agents based on layered spatio-temporal graph

In order to safely and comfortably navigate in the complex urban traffic, it is necessary to make multi-modal predictions of autonomous vehicles for the next trajectory of various traffic participants, with the continuous movement trend and inertia of the surrounding traffic agents taken into account. At present, most trajectory prediction methods focus on prediction on future behavior of traffic agents but with limited, consideration of the response of traffic agents to the future behavior of the ego-agent. Moreover, it can only predict the trajectory of single-type agents, which make it impossible to learn interaction in a complex environment between traffic agents. In this paper, we proposed a graph-based heterogeneous traffic agents trajectory prediction model LSTGHP, which consists of the following three parts: (1) layered spatio-temporal graph module; (2) ego-agent motion module; (3) trajectory prediction module, which can realize multi-modal prediction of future trajectories of traffic agents with different semantic categories in the scene. To evaluate its performance, we collected trajectory datasets of heterogeneous traffic agents in a time-varying, highly dynamic urban intersection environment, where vehicles, bicycles, and pedestrians interacted with each other in the scene. It can be drawn from experimental results that our model can improve its prediction accuracy while interacting at a close range. Compared with the previous prediction methods, the model has less prediction error in the trajectory prediction of heterogeneous traffic agents.

TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.

Comparative Analysis of Variants of Token Bucket Traffic Meter Algorithms for QoS Router using UDP as Traffic Agent

Traffic meter algorithms serve as a means of examining traffic stream’s conformance with service level agreement between customers (traffic sources) and Internet Service provider at the edge router of a differentiated service domain for proper quality of service admission control. This paper presented comparative analysis of variants of token bucket meter algorithms for QoS router using user datagram protocol (UDP) as traffic agents and exponential ON/OFF as traffic generator. The research adopted simulation technique to carry out the design of network models or topologies using the same parameter setting to implement the algorithm of token bucket variants of traffic meter. The following metrics were used for the evaluation: throughput, fairness rate, loss rate and one-way packet delay. The evaluated results were ranked and further subjected to 2-way analysis of variance (ANOVA) model to indicate the significant differences among the traffic meter algorithms. Based on ranking system, TRTCM was ranked first in terms of throughput (with 67117) and fairness rate (with 0.2586) and TBM was ranked first in terms of loss rate (with 74.003) and one-way packet delay (with 0.09304). The 2-way ANOVA model showed the significant differences among the traffic meter algorithms considered for the simulation.