A unified iLQR-based racing controller that blends historical lap data with soft obstacle-avoidance penalties overtakes more simulated opponents than LMPC baselines at lower compute.
Reach- ing the limit in autonomous racing: Optimal control versus reinforcement learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance
A unified iLQR-based racing controller that blends historical lap data with soft obstacle-avoidance penalties overtakes more simulated opponents than LMPC baselines at lower compute.