Research on intelligent vehicle collision avoidance strategy based on uncertainty of surrounding vehicle trajectory prediction
This paper proposes a research method for an intelligent vehicle collision avoidance strategy based on the uncertainty of trajectory prediction of surrounding vehicles.The trajectory prediction module combines physics-based trajectory prediction models with data-driven models to construct a physics-guided trajectory prediction model(PG-LSTM).The model outputs parameters of a two-dimensional Gaussian distribution for the predicted trajectories of surrounding vehicles to represent the uncertainty of drivers'behaviors.The risk assessment and collision avoidance strategy module,leveraging the output of the trajectory prediction model,introduces a new risk metric-Predictive Driving Risk(PDR)and Predictive Relative Driving Risk Index(PRDRI)as reference indicators for assessing future risks,establishing a collision avoidance decision-making mechanism for emergent situations.Complex emergency scenarios are simulated using Carsim.Our results indicate the proposed driving risk assessment model accurately identifies future driving risks in complex driving scenarios.Moreover,the collision avoidance decision mechanism based on driving risk enhances the collision avoidance safety of intelligent vehicles.
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