Research On Microscopic Behavior Modeling And Simulation Method Of Intelligent Vehicles In Mixed Traffic Environment

Throughout the progression of human civilization,transportation has played a pivotal role,constituting an intriguing undercurrent within the broader narrative of worldwide industrialization.Driven by emerging transportation development policies and state-of-the-art intelligent technologies,vehicles are developing towards intelligence and connectivity.In the future,the integration of automated vehicles within human drivers is an inevitable trend.Compared with legacy vehicles,intelligent vehicles have the significant advantages,such as shorter reaction times and more intelligent decision-making and trajectory-planning ability,which directly influence the operation performance of the overall traffic system.However,the microcosmic heterogeneous driving behaviors of intelligent vehicles and legacy vehicles are not discussed by previous works in detail and remain still unclear.Therefore,it is difficult to scientifically predict the complex interactive process of heterogeneous traffic flow.In addition,the macro and micro behavior research methods in traffic field study the traffic flow from different perspectives.The single research method is difficult to consider the accurate driving characteristics of individual vehicles and the dynamic evolution trend of the whole traffic flow.Therefore,research on finegrained micro behavior modeling method and corresponding simulation method for intelligent vehicles are necessary and urgent to simulate,analysis and optimize the efficiency,safety of heterogeneous traffic flow.To address these challenges,this dissertation focuses on mixed traffic flow scenario including intelligent vehicles and legacy vehicles.This scenario is characterized by diverse driving behavior patterns,including the experiential behaviors of human drivers and the automated decision-making of intelligent vehicles.These different behavior features enhance the uncertainty and complexity of traffic flow.Furthermore,the cooperation and information sharing mechanism among intelligent vehicles have additional influence on the efficiency and safety of traffic flow.Therefore,this dissertation takes the behavior of intelligent vehicles as a start point to explore the inherent interaction logic and operational characteristics between intelligent vehicles and legacy vehicles.This dissertation aims to investigate the individual microscopic behavior influence of intelligent vehicles on the dynamics of mixed traffic flow.The main research contents and achievements of this dissertation are as follows:(1)Intelligent Vehicle Driving Behavior Modeling Method for Typical Traffic Interaction ScenariosTo solve the fine-grained modelling and behavior analysis problems of intelligent vehicle in the typical traffic scenario,this section proposed a trajectory planning methodology based on two-dimensional simulation scenario,which considers the driving cost,dynamics constrains and traffic scenario requirements.Based on this methodology,the distribution patterns of trajectory cost surfaces within the trajectory sampling space were analysis.Furthermore,the optimization algorithms were employed to accelerate the searching speed of the optimal trajectory search.The behavior trajectory features of intelligent vehicles in various interaction scenarios were simulated and the effectiveness of the proposed behavior model was validated.Additionally,the proposed model has better solving efficiency by comparing with exhaustive sampling method.(2)Intelligent Vehicle Behavior Decision Method Based on Trajectory Data Analysis and Scenario PredictionTo reveal the underlying mechanisms of driving behaviors of intelligent vehicles,this section undertakes an analysis and exploration of real vehicle trajectory data and corresponding behavior characteristics.Based on these characteristics,a vehicle behavior prediction model was proposed based on an encoder-decoder model architecture.The effectiveness of this prediction model was validated through precision assessments and compared with field data.Furthermore,a state-transition-based speed planning model was proposed.This model provides velocity reference for the longitudinal motion planning in two-dimensional traffic simulation scenarios.Finally,considering the decision-making process of human drivers and latent driving intentions with lane-changing feasibility,a dual-layer decision mechanism for lane-changing behaviors was established.Comparative assessments with the behaviors of human drivers in real scenarios confirmed the rationality of this decision mechanism.(3)Driving Behavior Modeling Method for Intelligent Vehicle Platooning in Collaborative ScenariosIn the collaborative driving scenarios with multiple intelligent vehicles,this section analyzes the spontaneous adjustment process of multi-vehicles based on car-following theory.The key control objectives of vehicle platooning convergence were identified based on the interactive and platooning process between vehicles.An intelligent vehicle deviation compensation control method based on sliding mode control theory was proposed,with its stability proven through mathematical derivations.A cooperative control strategy for multi-intelligent vehicles in a single dimension queue was presented.Furthermore,by analyzing the effect of speed disturbances within vehicle platooning,a leadership strategy was introduced to enhance the platoon stability.Finally,the effectiveness of the proposed model was validated through simulation of intelligent vehicle platooning dynamics in various speed scenarios.(4)Simulation and Analysis of Mixed Traffic Flow on Highways Considering Microscopic influence of Intelligent VehicleTo address the challenges of precise behavioral representation and dynamic feature simulation in mixed traffic flow simulation scenarios,this dissertation integrates the previously proposed micro-level traffic behaviors models of intelligent vehicle.This dissertation employed a decision-planning iteration framework to capture the microscopic behaviors of intelligent vehicles in mixed traffic interaction scenarios.Combining classical driver behavior models,the simulation iteratively updated complex heterogeneous interaction scenarios.Compared to traditional simulation environments,this simulation environment reproduced micro-level behavior models with two-dimensional characteristics to calculate vehicle states in real-time.Simultaneously,it provides fine-grained simulation of the decision-making processes and behavior patterns between intelligent vehicles and legacy vehicles in coupled interaction scenarios.Building upon this foundation,the visualization and analysis of results from dynamic simulations of mixed traffic flow explored the impact of intelligent vehicle micro-level behavior in local traffic environments.This study proposed a novel approach for the precise representation of traffic scenario and visualized simulation of the mixed traffic flow scenarios.A two-dimensional motion planning model for intelligent vehicles were established for typical driving scenario.The reasoning process between driving scenario recognition,interactive decision-making and behavior generation was analysis.The micro-level traffic dynamics of complex interaction scenarios within mixed traffic environments was simulated.The achievements of this dissertation not only offer a theoretical foundation for modeling trajectory planning and decision-making behaviors of intelligent vehicles,but also break through the constraints of traditional simulation models which struggle to balance micro-behavior characteristics and traffic flow dynamics.The above achievements provide a methodological basis for assessing the efficiency,safety,and environmental impact of future transportation systems.