The Research On Autonomous Vehicle Platoon Algorithm Based On V2X

As an emerging direction of the integration of communication electronics,transportation and automotive industries,Vehicle-to-everything(V2X),emerging technologies such as 5G and artificial intelligence,will jointly achieve major breakthroughs in the traditional automotive and transportation industries.Autonomous driving is also a product of the deep integration of artificial intelligence and automotive electronics.It is being promoted and integrated into the development of new energy vehicles and intelligent vehicles,and it is constantly absorbing the most advanced artificial intelligence technology,bursting with strong vitality.With the development of the V2 X and autonomous driving technologies,industrial and social developments have put forward higher requirements for their applications,and a series of related industries and directions have been promoted.Among them,vehicle formation is the direction of the combined development of autonomous driving and Internet of Vehicles technologies.Received widespread attention in the industry.V2X technology is an important part of the realization of vehicle formations today and even in the future,and it mainly includes communications between vehicles,vehicles,roads,and cloud,and realizes the sharing and dissemination of information between each other through standard communication protocols.For vehicle formations,effective state synchronization and information transmission are essential.The vehicle formation needs to ensure the overall driving stability of the queue,and the cooperation between the vehicles,which requires synchronization of state and information with each other.As for the formation of self-driving cars,since each vehicle makes independent decisions and plans,it may cause conflict or instability in the overall decision-making of the fleet.Therefore,in this article,it is proposed to synchronize the decision-making and planning results of the bicycle as the communication information in the queue,and the leading vehicle as the main body of communication and the main body of the formation.At the same time,it is responsible for the communication within the team and the communication outside the team,and ensures that the vehicles in the queue make decisions automatically Consistency and robustness,thereby generating a reasonable planning curve.Through multiple autonomous vehicles to communicate and cooperate with each other to form a formation,the overall operating cost,efficiency and environmental protection of the fleet can be effectively improved.The existing application of autonomous driving technology mainly refers to bicycle intelligence,which mainly relies on the lidar,vision,millimeter wave radar and other sensors on the vehicle to sense the environment,and make decision planning and control execution based on the data obtained,including the driving route of the vehicle and the trajectory mainly depends on the decision planning algorithm.At present,the automatic driving planning algorithms used in the industry are mainly based on the sampling and optimization algorithms.Besides,most of these algorithms adopt the method of path-speed decoupling.For example,Baidu Apollo uses the Lattice Planner algorithm based on the Frenet coordinate system.However,one disadvantage of this method is that it makes the real planned route fail to effectively reflect the decision.This is because the route planning layer does not consider the execution of the speed plan,which makes the real route fail to meet the expectations of the speed plan.Therefore,this paper first proposes an STL space-time coordinate system-based trajectory planning algorithm for vehicle platooning according to the path-speed decoupling algorithm of the Frenet coordinate system.Furthermore,an angle anchor pointing method is designed to characterize the decisionmaking of the STL space-time coordinate system,which ensures the coordination of the path and speed planning.Besides,the optimal trajectory planning curve can be generated based on the angle anchor pointing approach.The state of vehicle formation can be roughly divided into two types: queue topology stability and queue topology adjustment.The former state guarantees stability and safety of the moving vehicle platoon by dynamic adjusting the speed and acceleration.The latter state adjusts the topology of the platoon when there are vehicles entering or leaving the platoon by performing the local trajectory planning.This paper proposes global speed planning and local trajectory planning by decomposing the formation algorithm into vertical formation and horizontal formation.The global speed planning adjusts the efficiency,safety and stability of moving of the platoon.The local trajectory planning dynamically adjusts the topology of the platoon according to the realtime road conditions and the requirements of the platoon.