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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$9K - $12K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

M

Mohamed Elgouhary

West Virginia University

A

Amr S. El-Wakeel

West Virginia University

Find Similar Experts

Autonomous experts on LinkedIn & GitHub

Founder's Pitch

"Optimize Pure Pursuit parameters using RL to improve autonomous vehicle path tracking efficiency in real-time."

Autonomous VehiclesScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

This research addresses a critical challenge in autonomous racing by optimizing Pure Pursuit parameters using reinforcement learning, enhancing path tracking performance without complex recalibrations for different tracks or conditions.

Product Angle

Develop a software module that integrates with existing autonomous vehicle control systems, providing a plug-and-play enhancement for vehicle path tracking using RL-optimized Pure Pursuit tuning.

Disruption

This solution offers a superior alternative to classical Pure Pursuit methods, reducing the need for manual tuning across diverse driving conditions and track profiles while maintaining simplicity and real-time efficiency, potentially replacing outdated path tracking methods.

Product Opportunity

The autonomous vehicle market is constantly seeking improvements in navigation efficiency and accuracy, particularly in racing and high-speed environments. Organizations and developers in autonomous driving sectors would pay for solutions that reduce human intervention and improve operational efficiency.

Use Case Idea

Implement this adaptive tuning of Pure Pursuit in real-world autonomous vehicles to improve path tracking and driving efficiency under variable conditions, minimizing human intervention for parameter setting, especially useful in racing or high-performance applications.

Science

The paper presents a reinforcement learning approach using Proximal Policy Optimization (PPO) to dynamically adjust the Pure Pursuit parameters—lookahead distance and steering gain—based on real-time observations of vehicle speed and path curvature. This adaptive tuning is shown to outperform traditional fixed or hand-tuned Pure Pursuit implementations.

Method & Eval

The approach was tested in both simulation using the F1TENTH platform and on real vehicles. It was compared against fixed-lookahead Pure Pursuit, adaptive velocity-scheduled variants, and MPC raceline tracker. It showed improvements in lap time, path-tracking accuracy, and steering smoothness.

Caveats

The approach may face challenges in real-world scalability across different vehicle types and driving conditions without further tuning. Safety measures need consideration to handle RL policy failures or stale commands effectively.

Author Intelligence

Mohamed Elgouhary

West Virginia University
mae00018@mix.wvu.edu

Amr S. El-Wakeel

West Virginia University
amr.elwakeel@mail.wvu.edu

References (21)

[1]
Hybrid Path Tracking Control for Autonomous Trucks: Integrating Pure Pursuit and Deep Reinforcement Learning With Adaptive Look-Ahead Mechanism
2025Zhixuan Han, Peng Chen et al.
[2]
Adaptive Pure Pursuit with Deviation Model Regulation for Trajectory Tracking in Small-Scale Racecars
2025Ralph Al Fata, Naseem A. Daher
[3]
Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System
2025Rasananda Muduli, Debashisha Jena et al.
[4]
Curvature Sensitive Modification of Pure Pursuit Control
2024Alexander L. Garrow, Diane L. Peters et al.
[5]
A Comprehensive Review on Deep Learning-Based Motion Planning and End-to-End Learning for Self-Driving Vehicle
2024Manikandan Ganesan, S. Kandhasamy et al.
[6]
Adaptive control and reinforcement learning for vehicle suspension control: A review
2024Jeremy B. Kimball, Benjamin DeBoer et al.
[7]
Comparing deep reinforcement learning architectures for autonomous racing
2023B. D. Evans, H. W. Jordaan et al.
[8]
Explainable Reinforcement Learning: A Survey and Comparative Review
2023Stephanie Milani, Nicholay Topin et al.
[9]
Adaptive Look-Ahead Distance Based on an Intelligent Fuzzy Decision for an Autonomous Vehicle
2023Fadel Tarhini, R. Talj et al.
[10]
How Simulation Helps Autonomous Driving: A Survey of Sim2real, Digital Twins, and Parallel Intelligence
2023Xuemin Hu, Shen Li et al.
[11]
Reward Bonuses with Gain Scheduling Inspired by Iterative Deepening Search
2022Taisuke Kobayashi
[12]
Stable-Baselines3: Reliable Reinforcement Learning Implementations
2021A. Raffin, Ashley Hill et al.
[13]
A path-tracking algorithm using predictive Stanley lateral controller
2020Ahmed Abdelmoniem, Ahmed Osama et al.
[14]
Minimum curvature trajectory planning and control for an autonomous race car
2020Alexander Heilmeier, Alexander Wischnewski et al.
[15]
Optuna: A Next-generation Hyperparameter Optimization Framework
2019Takuya Akiba, Shotaro Sano et al.
[16]
F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning
2019Matthew O'Kelly, Hongrui Zheng et al.
[17]
Proximal Policy Optimization Algorithms
2017John Schulman, Filip Wolski et al.
[18]
CDDT: Fast Approximate 2D Ray Casting for Accelerated Localization
2017Corey H. Walsh, S. Karaman
[19]
Real-time loop closure in 2D LIDAR SLAM
2016Wolfgang Hess, Damon Kohler et al.
[20]
Asynchronous Methods for Deep Reinforcement Learning
2016Volodymyr Mnih, Adrià Puigdomènech Badia et al.

Showing 20 of 21 references