Difference between revisions of "Simulation of vehicle platoon braking"

Latest revision as of 15:14, 30 September 2019

Generate PDF template

A platoon (or road train) is a collection of vehicles that cooperate to reach some common goal, such as travelling to a certain common destination. The platoon is led and coordinated by a lead vehicle, manually or automatically driven. Longitudinal and lateral control can be automated in the following vehicles. Some manoeuvres, such as driving with short intervehicle gaps and joining the platoon from the side, may imply that a human drive is not capable enough and control and coordination must hence be automated by the platoon. CACC is similar to platooning but may have less coordination between vehicles and also less degree of automation, e.g. it may lack lateral automation. A challenge is coordination in a situation where a vehicle in the platoon performs an emergency brake. The overall motivation is to avoid collisions within the platoon while still performing braking as efficiently (i.e. as high retardation) as possible. The simulator implements (in java) a model for each platoon vehicle as well as a communication framework for inter-vehicle (V2V) communication. The message loss model can be based on measured data or random loss. The platooning simulator is capable of simulating an N-vehicle platoon travelling in one dimension along a roadway. A scenario is controlled with simple inputs of: accelerate %, decelerate % and emergency brake. Scenario parameters are monitored to gather statistics of the outcome. A key control algorithm in the platooning simulator is the longitudinal position controller. For this, an CACC algorithm is implemented to control the component of each vehicle. In the simulator there is a detailed model of vehicle braking. This includes a model of a brake-by-wire subsystem featuring: (i) global brake torque distribution to individual wheels, (ii) ABS functionality based on slippage detection, and (iii) a friction model for tyres based on slippage rate using common physical parameter values. Environment models in the simulator deal with air resistance and road friction. Suggestion of contribution: • Implementation of emergency brake strategy with different communication strategies. This should be realistic, in terms of what communication models are given by the assumed communication mode: e.g. ITS-G5 or 5G. • We were interested to understand the performance of the algorithm, in combination with message packet loss, during emergency braking. The simulator allows different parameters such as message loss and headway to be modulated. E.g. studying achieved brake distance and probability of crash at a certain headway. To attain these values experimentally it is suitable to use a computing cluster.