Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted actively.The autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing units.To solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation nodes.However,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their complexity.They are also challenging to process computation tasks within 100 ms which is the time limit for driving safety.In this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving safety.The proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of computation.We also conduct simulations in the highway environment to evaluate the existing scheme and the proposed scheme.The result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.

Longitudinal and lateral control methods from single vehicle to autonomous platoon

To successfully implement the platoon control of connected and automated vehicles,it is necessary to address motion control issues to achieve longitudinal and lateral collaborative control.However,due to traffic capacity limitations and the complex traffic environment in which autonomous and human-driven vehicles coexist,autonomous platoon faces significant risks and challenges.This paper investigates longitudinal and lateral control issues from the perspective of a single vehicle up to a platoon,simulating the performance and suitability of various controllers.First,a longitudinal controller based on fuzzy logic and PID control is employed for speed tracking control of a single vehicle,followed by the adoption of an MPC controller based on the vehicle kinematics model to realize the lateral motion of a single vehicle.Second,the communication methods of the autonomous platoon are discussed,and the longitudinal controller that considers the platoon's various communication topologies is developed.Thirdly,a framework for robust integrated motion control is established,which combines the robust H-infinity longitudinal controller and the APF-based MPC lateral controller.Simulation results validate the effectiveness of the aforementioned controllers and reveal their limitations.

Large-Scale Vehicle Platooning:Advances and Challenges in Scheduling and Planning Techniques

Through vehicle-to-vehicle(V2V)communication,autonomizing a vehicle platoon can significantly reduce the distance between vehicles,thereby reducing air resistance and improving road traffic efficiency.The gradual maturation of platoon control technology is enabling vehicle platoons to achieve basic driving functions,thereby permitting large-scale vehicle platoon scheduling and planning,which is essential for industrialized platoon applications and generates significant economic benefits.Scheduling and planning are required in many aspects of vehicle platoon operation;here,we outline the advantages and challenges of a number of the most important applications,including platoon formation scheduling,lane-change planning,passing traffic light scheduling,and vehicle resource allocation.This paper’s primary objective is to integrate current independent platoon scheduling and planning techniques into an integrated architecture to meet the demands of large-scale platoon applications.To this end,we first summarize the general techniques of vehicle platoon scheduling and planning,then list the primary scenarios for scheduling and planning technique application,and finally discuss current challenges and future development trends in platoon scheduling and planning.We hope that this paper can encourage related platoon researchers to conduct more systematic research and integrate multiple platoon scheduling and planning technologies and applications.

Highway Toll Collection Method for Connected Automated Vehicle Platooning Using Spatio-Temporal Grid Reservation

In the intelligent transportation system, the autonomous vehicle platoon is a promising concept for addressing traffic congestion problems. However, under certain conditions, the platoon’s advantage cannot be properly developed, especially when stopping for electronic toll collection (ETC) to pay the toll fee using the highway. This study proposes a software architectural platform that enables connected automated vehicles to reserve a grid-based alternative approach to replace current highway toll collection systems. A planned travel route is reserved in advance by a connected automated vehicle in a platoon, and travel is based on reservation information. We use driving information acquired by communication mechanisms installed in connected automated vehicles to develop a dynamic map platform that collects highway toll tax based on reserving spatio-temporal grids. Spatio-temporal sections are developed by dividing space and time into equal grids and assigning a certain road tax rate. The results of the performance evaluation reveal that the proposed method appropriately reserves the specified grids and collects toll taxes accurately based on a spatio-temporal grid with minimal communication time and no data package loss. Likely, using the proposed method to mediate driving on a one-kilometer route takes an average of 36.5 seconds, as compared to ETC and the combination of ETC and freeway road lane methods, which take 46.6 and 53.8 seconds, respectively, for 1000 vehicles. Consequently, our proposed method’s travel time improvements will reduce congestion by more effectively exploiting road capacity as well as enhance the number of platoons while providing non-stoppable travel for autonomous vehicles.