TY - GEN
T1 - A software architecture for the dynamic path planning of an autonomous racecar at the limits of handling
AU - Betz, Johannes
AU - Wischnewski, Alexander
AU - Heilmeier, Alexander
AU - Nobis, Felix
AU - Hermansdorfer, Leonhard
AU - Stahl, Tim
AU - Herrmann, Thomas
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Based on a software architecture for autonomous driving presented and tested in an autonomous level-5 race-car in 2018 this paper describes in detail the evolutionary enhancement of this software architecture. The architecture combines the autonomous software layers perception, planning and control, which were modularized in the core software. The focus of this paper is the detailed description of how we enhanced the software with a module for an object list creation, a module for the behavioral planning and a module for the creation of dynamic trajectories. These enhancements allow the car to overtake other cars and static obstacles autonomously when driving on the race track. Furthermore, we present with a high novelty value the software module for a vehicle performance maximization, which consists of a control performance assessment and a friction estimation. The software architecture displayed in this paper will be tested and evaluated in the Roborace Season Alpha in 2019.
AB - Based on a software architecture for autonomous driving presented and tested in an autonomous level-5 race-car in 2018 this paper describes in detail the evolutionary enhancement of this software architecture. The architecture combines the autonomous software layers perception, planning and control, which were modularized in the core software. The focus of this paper is the detailed description of how we enhanced the software with a module for an object list creation, a module for the behavioral planning and a module for the creation of dynamic trajectories. These enhancements allow the car to overtake other cars and static obstacles autonomously when driving on the race track. Furthermore, we present with a high novelty value the software module for a vehicle performance maximization, which consists of a control performance assessment and a friction estimation. The software architecture displayed in this paper will be tested and evaluated in the Roborace Season Alpha in 2019.
UR - http://www.scopus.com/inward/record.url?scp=85076090190&partnerID=8YFLogxK
U2 - 10.1109/ICCVE45908.2019.8965238
DO - 10.1109/ICCVE45908.2019.8965238
M3 - Conference contribution
AN - SCOPUS:85076090190
T3 - 2019 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 - Proceedings
BT - 2019 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019
Y2 - 4 November 2019 through 8 November 2019
ER -