Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Samak, Tanmay"'
Contrary to on-road autonomous navigation, off-road autonomy is complicated by various factors ranging from sensing challenges to terrain variability. In such a milieu, data-driven approaches have been commonly employed to capture intricate vehicle-e
Externí odkaz:
http://arxiv.org/abs/2409.10347
Societal-scale deployment of autonomous vehicles requires them to coexist with human drivers, necessitating mutual understanding and coordination among these entities. However, purely real-world or simulation-based experiments cannot be employed to e
Externí odkaz:
http://arxiv.org/abs/2406.05465
Autor:
Samak, Tanmay Vilas, Samak, Chinmay Vilas, Binz, Joey, Smereka, Jonathon, Brudnak, Mark, Gorsich, David, Luo, Feng, Krovi, Venkat
Publikováno v:
SAE Technical Paper 2024-01-4111
Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Traditional methods focusing on sequentially sweeping across the parameter space for variability analysis struggle to compre
Externí odkaz:
http://arxiv.org/abs/2405.04743
Multi-agent reinforcement learning (MARL) systems usually require significantly long training times due to their inherent complexity. Furthermore, deploying them in the real world demands a feature-rich environment along with multiple embodied agents
Externí odkaz:
http://arxiv.org/abs/2403.10996
Modeling and simulation of autonomous vehicles plays a crucial role in achieving enterprise-scale realization that aligns with technical, business and regulatory requirements. Contemporary trends in digital lifecycle treatment have proven beneficial
Externí odkaz:
http://arxiv.org/abs/2402.14739
Publikováno v:
2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA, 2024, pp. 1068-1075
Autonomous vehicle platforms of varying spatial scales are employed within the research and development spectrum based on space, safety and monetary constraints. However, deploying and validating autonomy algorithms across varying operational scales
Externí odkaz:
http://arxiv.org/abs/2402.12670
Publikováno v:
IEEE/ASME Transactions on Mechatronics, vol. 29, no. 4, pp. 2785-2793, 2024
Simulation to reality (sim2real) transfer from a dynamics and controls perspective usually involves re-tuning or adapting the designed algorithms to suit real-world operating conditions, which often violates the performance guarantees established ori
Externí odkaz:
http://arxiv.org/abs/2401.11542
This work presents a modular and parallelizable multi-agent deep reinforcement learning framework for imbibing cooperative as well as competitive behaviors within autonomous vehicles. We introduce AutoDRIVE Ecosystem as an enabler to develop physical
Externí odkaz:
http://arxiv.org/abs/2309.10007
Publikováno v:
Elsevier IFAC-PapersOnLine, vol. 56, no. 3, pp. 277-282, 2023
The engineering community currently encounters significant challenges in the development of intelligent transportation algorithms that can be transferred from simulation to reality with minimal effort. This can be achieved by robustifying the algorit
Externí odkaz:
http://arxiv.org/abs/2307.13272
Publikováno v:
2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Seattle, WA, USA, 2023, pp. 1208-121
Modern-day autonomous vehicles are increasingly becoming complex multidisciplinary systems composed of mechanical, electrical, electronic, computing and information sub-systems. Furthermore, the individual constituent technologies employed for develo
Externí odkaz:
http://arxiv.org/abs/2301.13425