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pro vyhledávání: '"Ghignone A"'
Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
Autor:
Baumann, Nicolas, Ghignone, Edoardo, Hu, Cheng, Hildisch, Benedict, Hämmerle, Tino, Bettoni, Alessandro, Carron, Andrea, Xie, Lei, Magno, Michele
Head-to-head racing against opponents is a challenging and emerging topic in the domain of autonomous racing. We propose Predictive Spliner, a data-driven overtaking planner that learns the behavior of opponents through Gaussian Process (GP) regressi
Externí odkaz:
http://arxiv.org/abs/2410.04868
Autor:
Baumann, Nicolas, Baumgartner, Michael, Ghignone, Edoardo, Kühne, Jonas, Fischer, Tobias, Yang, Yung-Hsu, Pollefeys, Marc, Magno, Michele
To enable self-driving vehicles accurate detection and tracking of surrounding objects is essential. While Light Detection and Ranging (LiDAR) sensors have set the benchmark for high-performance systems, the appeal of camera-only solutions lies in th
Externí odkaz:
http://arxiv.org/abs/2403.15313
Autor:
Baumann, Nicolas, Ghignone, Edoardo, Kühne, Jonas, Bastuck, Niklas, Becker, Jonathan, Imholz, Nadine, Kränzlin, Tobias, Lim, Tian Yi, Lötscher, Michael, Schwarzenbach, Luca, Tognoni, Luca, Vogt, Christian, Carron, Andrea, Magno, Michele
Autonomous racing in robotics combines high-speed dynamics with the necessity for reliability and real-time decision-making. While such racing pushes software and hardware to their limits, many existing full-system solutions necessitate complex, cust
Externí odkaz:
http://arxiv.org/abs/2403.11784
This work introduces SynPF, an MCL-based algorithm tailored for high-speed racing environments. Benchmarked against Cartographer, a state-of-the-art pose-graph SLAM algorithm, SynPF leverages synergies from previous particle-filtering methods and syn
Externí odkaz:
http://arxiv.org/abs/2401.07658
Range-measuring sensors play a critical role in autonomous driving systems. While LiDAR technology has been dominant, its vulnerability to adverse weather conditions is well-documented. This paper focuses on secondary adverse conditions and the impli
Externí odkaz:
http://arxiv.org/abs/2309.10504
Publikováno v:
Applied Computing and Geosciences, Vol 23, Iss , Pp 100186- (2024)
An analytical method to automatically characterize rock samples for geological or petrological purposes is here proposed, by applying machine learning approach (ML) as a protocol for saving experimental times and costs.Proper machine learning algorit
Externí odkaz:
https://doaj.org/article/e2958b4c30c94881b4ea2cbc25e0cccc
Autor:
Becker, Jonathan, Imholz, Nadine, Schwarzenbach, Luca, Ghignone, Edoardo, Baumann, Nicolas, Magno, Michele
Publikováno v:
2023 IEEE International Conference on Robotics and Automation (ICRA)
Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving. For scaled autonomous racing platforms, the computational constraint a
Externí odkaz:
http://arxiv.org/abs/2209.04346
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-8 (2024)
Abstract Distinguishing syngenetic from protogenetic inclusions in natural diamonds is one of the most debated issues in diamond research. Were the minerals that now reside in inclusions in diamonds born before the diamond that hosts them (protogenes
Externí odkaz:
https://doaj.org/article/e5b4d9e8ed6f4652af1fafe896316833
Publikováno v:
Field Robotics 2023
Autonomous racing is becoming popular for academic and industry researchers as a test for general autonomous driving by pushing perception, planning, and control algorithms to their limits. While traditional control methods such as MPC are capable of
Externí odkaz:
http://arxiv.org/abs/2205.09370
Publikováno v:
In Applied Computing and Geosciences September 2024 23