Trajectory Planning for Autonomous Vehicles and Cooperative Driving
Abstract: Autonomous vehicles have been the subject of intense research, resulting in many of the latest cars being at least partly self driving. Cooperative driving extends this to a group of vehicles called a platoon, relying on com-munication between the vehicles in order to increase safety and improve the ˛ow of tra°c. This thesis is partly done in context of Grand Cooperative Driving Challenge (GCDC) 2016 where KTH has participated with a Scania truck and the Research Concept Vehicle (RCV), an electric prototype car.Trajectory planning is investigated for the longitudinal control of both the truck and the RCV. This planner is to ensure that the vehicles reached a position in a given time and a desired velocity. This is done using Pon-tryagin's minimum principle and interpolation.A more advanced planner based on Model Predictive Control (MPC) is used to avoid collisions in two di˙erent scenarios. One considers obstacle avoidance in the form of an overtake and the other a lane change scenario were the vehicle needs to decide how to position itself relative to the other vehicles.Simulations of the longitudinal control and planning of the truck did show that it could time the position and speed with a position error of less than 2m and speed error less than 0.2 m/s, assuming a distance of 120-200 m, a time interval of 40s and goal speed of 7m/s. The same simulation for the RCV had a distance error of less than 0.3m and a speed error below 0.2m.Simulations of the RCV using MPC planners showed that overtaking and lane changes could be performed. When performing the lane change the RCV managed to maintain a longitudinal distance of at least 1m, even if the other vehicles are slowing down or increasing their speed. The overtaking could also be successfully performed although with small margins, having a lateral distance of 0.5 m to the vehicle being overtaken.
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