DBN-MACTraj: Dynamic Bayesian Networks for Predicting Combinations of Long-Term Trajectories with Likelihood for Multiple Agents

Haonan Cui, Haolun Qi and Jianyu Zhou ()
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Haonan Cui: College of Software, Nankai University, Tianjin 300350, China
Haolun Qi: College of Software, Nankai University, Tianjin 300350, China
Jianyu Zhou: College of Software, Nankai University, Tianjin 300350, China

Mathematics, 2024, vol. 12, issue 23, 1-13

Abstract: Accurately predicting the long-term trajectories of agents in complex traffic environments is crucial for the safety and effectiveness of autonomous driving systems. This paper introduces DBN-MACTraj, a probabilistic model that takes historical trajectories and surrounding lane information as inputs to generate a distribution of predicted trajectory combinations for all agents. DBN-MACTraj consists of two main components: a single-agent probabilistic model and a multi-agent risk-averse sampling algorithm. The single-agent model utilizes a dynamic Bayesian network, which incorporates the driver’s maneuvering decisions along with information about surrounding lanes. The multi-agent sampling algorithm simultaneously generates predictions for all agents, using a risk potential field model to filter out samples that may lead to traffic accidents. Ultimately, this results in a probability distribution of the combinations of long-term trajectories. Experimental evaluations on the nuScenes dataset demonstrate that DBN-MACTraj delivers competitive performance in trajectory prediction compared to other state-of-the-art approaches.

Keywords: long-term trajectory prediction; dynamic Bayesian model; multi-agent system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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