Abstract
To enhance the reality of Connected and Autonomous Vehicles (CAVs) kinematic simulation scenarios and to guarantee the accuracy and reliability of the verification, a four-layer CAVs kinematic simulation framework, which is composed with road network layer, vehicle operating layer, uncertainties modelling layer and demonstrating layer, is proposed in this paper. Properties of the intersections are defined to describe the road network. A target position based vehicle position updating method is designed to simulate such vehicle behaviors as lane changing and turning. Vehicle kinematic models are implemented to maintain the status of the vehicles when they are moving towards the target position. Priorities for individual vehicle control are authorized for different layers. Operation mechanisms of CAVs uncertainties, which are defined as position error and communication delay in this paper, are implemented in the simulation to enhance the reality of the simulation. A simulation platform is developed based on the proposed methodology. A comparison of simulated and theoretical vehicle delay has been analyzed to prove the validity and the creditability of the platform. The scenario of rear-end collision avoidance is conducted to verify the uncertainties operating mechanisms, and a slot-based intersections (SIs) control strategy is realized and verified in the simulation platform to show the supports of the platform to CAVs kinematic simulation and verification.
Highlights
Traffic simulation software is widely used in verifying and optimizing the traffic coordinating algorithms, due to its characteristics of repeatability, maneuverability, accuracy and low-cost
Due to the operation mechanism of the traffic simulation software, sometimes it is very complicated for researchers to implement customized models or control strategies within the application program interfaces (APIs) constraints, or models and algorithms we implement in the software are not able to operate as we anticipate
connected and autonomous vehicles (CAVs) application, the methodology influence uncertainties to the pre-warning algorithms and a methodology proposed in this paper turns out to be reliable
Summary
Traffic simulation software is widely used in verifying and optimizing the traffic coordinating algorithms, due to its characteristics of repeatability, maneuverability, accuracy and low-cost. In CAVs simulation, based on the V2X communication and status perception, control strategies should be generated to each individual vehicle to better simulate the autonomy of the vehicle. Due to the operation mechanism of the traffic simulation software, sometimes it is very complicated for researchers to implement customized models or control strategies within the API constraints, or models and algorithms we implement in the software are not able to operate as we anticipate In this situation, fundamental simulation environment should be built to verify the models and the control strategies. The purpose of perceiving vehicle and environment status is to precisely judge the spatial and temporal relationship between two traffic objects, so the decision-making component can generate an optimal control strategy and better coordinate the traffic. A simulation platform based on the proposed methodology is developed, and experiments are conducted to prove the reliability of the methodology
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