# Guaranteeing Consistency in a Motion Planning and Control Architecture Using a Kinematic Bicycle Model

arXiv: https://arxiv.org/abs/1804.08290 · HTML: https://mlsanity.com/paper/1804.08290/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [PhysORD: A Neuro-Symbolic Approach for Physics-infused Motion Prediction in Off-road Driving](https://mlsanity.com/paper/2404.01596/) (2024)
- [Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic](https://mlsanity.com/paper/2310.02918/) (2023)
- [Towards Physics-informed Diffusion for Anomaly Detection in Trajectories](https://mlsanity.com/paper/2506.06999/) (2025)
- [Learning-based Observer Evaluated on the Kinematic Bicycle Model](https://mlsanity.com/paper/2303.17933/) (2023)
- [A Review of Autonomous Road Vehicle Integrated Approaches to an Emergency Obstacle Avoidance Maneuver](https://mlsanity.com/paper/2105.09446/) (2021)
- [Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation](https://mlsanity.com/paper/2607.06957/) (2026)

## Abstract (arXiv)

This paper proposes to combine a 10Hz motion planner based on a kinematic bicycle Model Predictive Control (MPC) and a 100Hz closed-loop Proportional-Integral-Derivative (PID) controller to cope with normal driving situations. Its novelty consists in ensuring the feasibility of the computed trajectory by the motion planner through a limitation of the steering angle depending on the speed. This ensures the validity of the kinematic bicycle model at any time. The architecture is tested on a high-fidelity simulation model on a challenging track with small curve radius, with and without surrounding obstacles.