Ehsan Javanmardi

The University of Tokyo

Papers

2

Total Citations

7

H-Index

2

About

Ehsan Javanmardi is a researcher advancing the frontiers of autonomous vehicle intelligence and human-robot interaction. His work centers on two critical challenges: enabling safe, high-speed autonomous racing through multi-agent coordination, and designing intuitive communication systems between self-driving cars and pedestrians. In his influential 2024 paper "RaceMOP," Javanmardi tackles the complex problem of mapless online path planning for multi-agent autonomous racing. By developing a residual policy learning framework, he overcomes the limited planning horizon that traditionally hinders safe overtaking maneuvers—a breakthrough with implications far beyond racing, extending to any dynamic multi-agent environment. This work has already garnered 5 citations, reflecting its timely impact. Complementing this technical innovation, his "Text + Eye" study (2024, 2 citations) introduces a novel mixed external human-machine interface (eHMI) for autonomous taxis. By combining text with a dynamic "eye" display, Javanmardi addresses the critical limitation of text-only systems: their inability to specify spatial targets like a particular person or location. This human-centric design bridges the gap between machine intent and human understanding, a vital step toward public trust in autonomous vehicles.

Research Focus

Key Achievements

2
H-Index
2
Papers
7
Total Citations
4
Avg Citations/Paper
🏆 Most Cited Paper
RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning
5 citations · 2024
📈 Most Prolific Year: 2024 (2 Papers)
🤝 Key Collaborators: 9
🏛 Institutions: The University of Tokyo

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 13 days ago