# Consecutive Inertia Drift of Autonomous RC Car via Primitive-based Planning and Data-driven Control

arXiv: https://arxiv.org/abs/2306.12604 · HTML: https://mlsanity.com/paper/2306.12604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control](https://mlsanity.com/paper/2312.05332/) (2023)
- [Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits](https://mlsanity.com/paper/2507.23339/) (2025)

## Abstract (arXiv)

Inertia drift is an aggressive transitional driving maneuver, which is challenging due to the high nonlinearity of the system and the stringent requirement on control and planning performance. This paper presents a solution for the consecutive inertia drift of an autonomous RC car based on primitive-based planning and data-driven control. The planner generates complex paths via the concatenation of path segments called primitives, and the controller eases the burden on feedback by interpolating between multiple real trajectories with different initial conditions into one near-feasible reference trajectory. The proposed strategy is capable of drifting through various paths containing consecutive turns, which is validated in both simulation and reality.