Research Problem

Whether BMW, VW or Google: almost all leading automotive groups and technology companies are researching and developing multi-stage autonomy for vehicles, which in autonomy level 5 enables a completely self-propelled vehicle without a driver. Based on his assessment and experience, the driver had so far carried out a detection of the environment, a localisation and optimal control of the vehicle. The elimination of the driver poses numerous challenges in the development of Level 5 vehicles.

Idea

With the start of the third Formula E series, a further support series called Roborace will take place on the tracks currently used by Formula E. The new Formula E series will also be available in the near future. The aim of Roborace is to offer the first racing series for electric autonomous cars. The teams participating in this competition will only develop the software for the autonomous cars (Robocars) provided. 

The Technical University of Munich (TUM) has decided to participate in this race series with its own team based on the knowledge of various institutes. The team wants to develop different functions for the operation of the autonomous racing car and evaluate them afterwards.

Goals

The goal of the project TUM-Roborace is the development of a software, which can move an autonomous Level-5 vehicle in the driving dynamic limit area on the track. In order to achieve this goal, the individual team members are working on subprojects, each of which contributes to the overall software architecture of the vehicle. The focus of the final real integration and testing in the Robocar is on the evaluation of the real-time capability, performance and reliability of the algorithms. Thus, the gained experiences can be used and finally statements for the further use of the developed functions in autonomous production vehicles can be made. The following subprojects are carried out within the TUM-Roborace project:

Results

2018

Winner of the "Human + Machine Challenge" together with Errolson Hugh:

2019

 

Publications

Betz, J.; Wischnewski, A.; Heilmeier, A.; Nobis, F.; Stahl, T.; Hermansdorfer, L.; Lohmann, B.; Lienkamp, M.; „What can we learn form autonomous level 5 Motorsport?“ at the 10th international Chassis Symposium “Chassis.Tech Plus 2019”, Munich, Juni 2018, doi: 10.1007/978-3-658-22050-1_12

Heilmeier, A.; Graf, M.; Lienkamp, M.;"A Race Simulation for Strategy Decisions in Circuit Motorsports" at the 21th International Conference on Intelligent Transportation Systems (ITSC) 2018, Hawaii, October 2018,doi: 10.1109/ITSC.2018.8570012

Heilmeier, A.; Wischnewski, A.; Hermansdorfer, L.; Betz, J.; Lienkamp, M.; Lohmann, B.:"Minimum curvature trajectory planning and control for an autonomous race car" in Vehicle System Dynamics - International Journal of Vehicle Mechanics and Mobility, pp. 1–31, June 2019, doi: 10.1080/00423114.2019.1631455

Stahl, T.; Wischnewski, A.; Betz, J.; Lienkamp, M: “ROS-based localization of a race vehicle at high-speed using LIDAR“ in E3S Web of Conferences, vol. 95, p. 4002, 2019, doi: 10.1051/e3sconf/20199504002

Betz, J.; Wischnewski, A.; Heilmeier, A.; Nobis, F.; Stahl, T.; Hermansdorfer, L.; Lienkamp, M.; „A Software Architecture for an Autonomous Racecar“ at the 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, doi: 10.1109/VTCSpring.2019.8746367

Nobis, F.; Betz, J. ; Hermansdorfer, L.; Lienkamp, M.: “Autonomous Racing: A Comparison of SLAM Algorithms for Large Scale Outdoor Environments“ in Proceedings of the 2019 3rd International Conference on Virtual and Augmented Reality Simulations - ICVARS ’19, 2019, doi: 10.1145/3332305.3332319

Palafox, P.; Betz, J.; Nobis, F.; Riedl, K.; Lienkamp, M.: "Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads Without Lane Lines" in Sensors, vol. 19, no. 14, p. 3224, Jul. 2019. doi: 10.3390/s19143224

Heilmeier, A.; Geisslinger, M.; Betz, J.;"A Quasi-Steady-State Lap Time Simulation for Electrified Race Cars" at the 14th International Conference on Ecological Vehicles and Renewable Energies (EVER2019), Monaco, 2019, doi: 10.1109/EVER.2019.8813646

Wischnewski, A.; Stahl, T.; Betz, J.; Lohmann, B.; „Vehicle Dynamics State Estimation and Localization for High Performance Race Cars “ IFAC-PapersOnLine, vol. 52, no. 8, pp. 154–161, 2019, doi: 10.1016/j.ifacol.2019.08.064

Stahl, T.; Wischnewski, A.; Betz, J.; Lienkamp, M: “Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios“ in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, doi: 10.1109/ITSC.2019.8917032

Hermansdorfer, L.; Betz, J.; Lienkamp, M: “ A Concept for Estimation and Prediction of the Tire-Road Friction Potential for an Autonomous Racecar“ in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, doi: 10.1109/ITSC.2019.8917024

Herrmann, T.; Christ, F.; Betz, J.; Lienkamp, M: “Energy Management Strategy for an Autonomous Electric Racecar using Optimal Control “ in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019, doi: 10.1109/ITSC.2019.8917154

Wischnewski, A.; Betz, J.; Lohmann, B.: “'A Model-Free Algorithm to Safely Approach the Handling Limit of an Autonomous Racecar“ in 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE 2019), Graz, Austria , 2019, doi: 10.1109/ICCVE45908.2019.8965218

Betz, J.; Wischnewski, A.; Heilmeier, A.; Nobis, F.; Stahl, T.; Hermansdorfer, L.; Herrmann, T.; Lienkamp, M.;: “A Software Architecture for the Dynamic Path Planning of an Autonomous Racecar at the Limits of Handling“ in 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE 2019), Graz, Austria , 2019, doi: 10.1109/ICCVE45908.2019.8965238

Nobis, F.; Geisslinger, M.; Weber, M.; Betz, J.; Lienkamp, M.: "Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection," in 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 10.1109/SDF.2019.8916629

Christ, F.; Wischnewski, A.; Heilmeier, A.; Lohmann, B.: "Time-Optimal Trajectory Planning for a Race Car Considering Variable Tire-Road Friction Coefficients" in Vehicle System Dynamics - International Journal of Vehicle Mechanics and Mobility, doi: 10.1080/00423114.2019.1704804

Betz, J.; Heilmeier, A.; Wischnewski, A.; Stahl, T.; Lienkamp, M.; “Autonomous Driving - A Crash Explained in Detail“ in Applied Sciences, vol. 9, no. 23, p. 5126, Nov. 2019, https://doi.org/10.3390/app9235126

Presentations

Matlab Expo: A Real-Time Simulation Environment for Autonomous Vehicles in Highly Dynamic Driving Scenarios

Nvidia GTC 2018 Munich: Roborace: A Case Study in Collaboration

Munich Science Days: Autonomous Driving and the New Worlds of Work - the Example of Roborace

Software

You can find our software stack we used in the Roborace Events in our Github Repository:

TUM Roborace Github Repositories

TUM Roborace Github Repository (control)

TUM Roborace Github Repository (raceline optimization)

TUM Roborace Github Repository (functions for trajectory planning)

 

Sponsoring

The TUM Roborace Team is supported in this research project by numerous companies and sponsors, which are listed below: