Pure Pursuit Lane Following with Obstacle Avoidance

Project Resources

Objective: Develop a lane following and obstacle avoidance system using the pure pursuit control for Duckiebots. (DB18)
Approach: Use an adaptive pure pursuit controller with dynamic speed tuning, integrated with vision-based deep-learning based object detection for obstacle-aware navigation.
Authors: Soroush Saryazdi, Dhaivat Bhatt, Robotics and Embodied AI Lab (REAL), Université de Montréal and is also affiliated with MILA.

Project highlights

Pure Pursuit Lane Following with Obstacle Avoidance - the objectives

Pure pursuit is a geometric path tracking algorithm used in autonomous vehicle control systems. It calculates the curvature of the road ahead by determining a target point on the trajectory and computing the required angular velocity to reach that point based on the vehicle’s kinematics.

Unlike proportional integral derivative (PID) control, which adjusts control outputs based on continuous error correction, pure pursuit uses a lookahead point to guide the vehicle along a trajectory, enabling stable convergence to the path without oscillations. This method avoids direct dependency on derivative or integral feedback, reducing complexity in environments with sparse or noisy error signals.

This project aims to implement a pure pursuit-based lane following system integrated with obstacle avoidance for autonomous Duckiebot navigation. The goal is to enable real-time tracking of lane centerlines while maintaining safety through detection and response to dynamic obstacles such as other Duckiebots or cones.

The pipeline includes a modified ground projection system, an adaptive pure pursuit controller for path tracking, and both image processing and deep learning-based object detection modules for obstacle recognition and avoidance.

Faster RCNN model architecture for pure pursuit and obstacle avoidance in Duckietown object detection — Faster RCNN Architecture with Feature Pyramid Network

Object detection results using Faster RCNN for pure pursuit and obstacle avoidance in Duckietown with bounding boxes for duckiebots and traffic cones — Faster RCNN Detection Output with Bounding Boxes

Duckietown object detection for pure pursuit and obstacle avoidance showing detected duckiebots and cones using deep learning — Detection Results for Obstacle Avoidance in Duckietown

The challenges and approach

The primary challenges in this project include robust target point estimation under variable lighting and environmental conditions, real-time object detection with limited computational resources, and smooth trajectory control in the presence of dynamic obstacles.

The approach involves modular integration of perception, planning, and control subsystems.

For perception, the system uses both classical image processing methods and a trained deep learning model for object detection, enabling redundancy and simulation compatibility.

For planning and control, the pure pursuit controller dynamically adjusts speed and steering based on the estimated target point and obstacle proximity. Target point estimation is achieved through ground projection, a transformation that maps image coordinates to real-world planar coordinates using a calibrated camera model. Real-time parameter tuning and feedback mechanisms are included to handle variations in frame rate and sensor noise.

Obstacle positions are also ground-projected and used to trigger stop conditions within a defined safety zone, ensuring collision avoidance through reactive control.

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Pure Pursuit Lane Following with Obstacle Avoidance: Authors

Soroush Saryazdi is currently leading the Neural Networks team at Matic, supervised by Navneet Dalal.

Dhaivat Bhatt is currently working as a Machine learning research engineer at Samsung AI centre, Toronto.

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