Marc Revilloud
Papers
1
Total Citations
23
H-Index
1
About
Marc Revilloud is a leading researcher in autonomous driving systems, with a primary focus on high-level decision-making for highway scenarios. His work bridges artificial intelligence and vehicle control, addressing the critical challenge of how autonomous vehicles interpret complex environments and make safe, real-time navigational choices. Revilloud’s most cited paper, “A study on AI-based approaches for high-level decision making in highway autonomous driving” (2017, 23 citations), provides a comprehensive state-of-the-art analysis of AI methods—from rule-based systems to learning-based approaches—for tactical decision-making in highway contexts. This foundational review has become a key reference for researchers developing autonomous driving architectures, helping to clarify the landscape of available techniques and their suitability for different operational domains. Beyond this seminal review, Revilloud’s broader contributions include advancing the integration of perception, planning, and control modules, with an emphasis on robust, scalable solutions for real-world deployment. His work continues to influence the design of autonomous vehicle systems, particularly in the critical area of high-level reasoning that determines vehicle behavior in dynamic traffic environments.
Research Focus
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Top Papers
- 1