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Trajectory Planning for Autonomous Vehicles and Cooperative Driving
KTH, School of Electrical Engineering (EES).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Abstract [sv]

Autonoma fordon har länge varit ett intensivt forskningsområde vilket resulterat i att många av de senaste bilana är åtminstone delvis självkörande. Cooperative driving utvidgar detta till en grupp fordon som kommunicerar med varandra för att öka säkerheten och få trafiken att flyta bättre. Den här uppsatsen baseras på Grand Cooperative Divers Challange (GCDC) 2016 där KTH deltog med en Scania lastbil och en elbil kallad Research Concept Vehicle (RCV).

Rörelseplannering har undersökts för longitudinell kontrol av lastbilen och RCV. Den här planeraren ska se till att fordonet når en given position inom en viss tid och med en önskad hastighet. För detta ändamål används vad som kallas "Pontryagin's minimum principle" och interpolation.

En mer avancerad planerare baserad på Model Predictive Control (MPC) används för att undvika kollisioner i två olika situationer. Den ena simulerar en omkörning och den andra ett filbyte med flera andra fordon i den intilliggande filen.

Simuleringar av den longitudinella kontrollen av lastbilen visade att den kunde nå en position och hastighet med ett fel mindre än 2m respektive 0,2m/s då sträckor mellan 120-200m, ett tidsintervall på 40s och önskad hastighet på 7m/s används. Samma simuleringar med RCV hade ett positionsfel mindre än 0,3m och hastighetsfel under 0,2m/s.

Simuleringar med RCV då MPC används visade att omkörningar och filbyten kunde genomföras. Filbyten kunde genomföras med ett longitudinellt avstånd på minst 1m, även då övriga fordon saktar ner eller ökar farten. Omkörningar kunde också genomföras om än med små marginaler. Det laterala avståndet var 0,5m till det omkörda fordonet.

Place, publisher, year, edition, pages
2016.
Series
EES Examensarbete / Master Thesis, TRITA EE 2016:119
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-194496OAI: oai:DiVA.org:kth-194496DiVA: diva2:1040724
Examiners
Available from: 2016-10-28 Created: 2016-10-28 Last updated: 2016-11-15Bibliographically approved

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