One of the cars spontaneously decided to drive into a … We may never know, but we did get to see a car drive straight into a wall and not hurt anyone in the process, and in that way, I declare the event a huge success.

You can find our software stack we used in the Roborace Events in our Github Repository: TUM Roborace Github Repository (raceline optimization), TUM Roborace Github Repository (functions for trajectory planning). 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. J: “A Scenario Generator for Evaluating Path Planning Algorithms for Autonomous Driving” in 15th International Conference on Ecological Vehicles and Renewable Energies (EVER2020), May 2020, Monaco, France, accepted, Fulltext (Preprint), Nobis, F.; Papanikolaoi, O.; Betz, J.; Lienkamp, M: “Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension” in 15th International Conference on Ecological Vehicles and Renewable Energies (EVER2020), May 2020, Monaco, France, accepted, Fulltext(Preprint), Hermansdorfer, L.; Betz, J.; Lienkamp, M: “Benchmarking of a software stack for autonomous racing against a professional human race driver” in 15th International Conference on Ecological Vehicles and Renewable Energies (EVER2020), May 2020, Monaco, France, accepted, Fulltext(Preprint), Heilmeier, A.; Graf, M.; Betz, J.; Lienkamp, M.: “Monte Carlo Simulation of Random Race Events in a Race Simulation for Circuit Motorsports” in Applied Sciences, doi: https://doi.org/10.3390/app10124229, Wischnewski, A.; Betz, J.; Lohmann, B.: „Real-Time Learning of Non-Gaussian Uncertainty Models for Autonomous Racing“ in 59th IEEE Conference on Decision and Control (CDC), Jeju Island, Republic of Korea, December 2020, accepted, Herrmann, T.; Wischnewski, A.; Hermansdorfer, L.; Betz, J.; Lienkamp, M.: „Real-Time Adaptive Velocity Optimization for Autonomous Electric Race Cars“ in IEEE Transactions on Intelligent Vehicles, under Review, Hermandorfer, L.; Trauth, R.; Betz, J.; Lienkamp, M.: „End-to-End Neural Network vor Vehicle Dynamics Modeling“ in 3rd IEEE Conference on Optimization and Modeling of Complex Systems, Agadir, Morocco, December 2020, under review, 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. WHATTT It's live https://t.co/LOFjjjXKkL pic.twitter.com/Jz5mxclmH5. 8, pp. 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.



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