Project Hub /Automotive EV & Transportation/Smart Vehicle Safety & ADAS/Raspberry Pi based Lane Detection System using Lidar

Intermediate

RASPBERRY PI

₹5,000 – ₹15,000

3 – 6 Hours

Partial Kit

Raspberry Pi based Lane Detection System using Lidar

This project details the creation of a LIDAR and Raspberry Pi (or Nvidia Jetson) powered lane and obstacle detection system intended for ADAS applications.

Originally published on ElectronicsForU

View original tutorial

Parts You'll Need

This project is sourced from another site, not written by us — this list is our best match of their components against what we stock, not a verified bill of materials. Always check the original blog (linked above) for the actual parts list before buying.

Raspberry Pi 3 - Model B+ 1.4Ghz 64-bit quad-core Processor with 1 GB Ram (Original) Rs. 9,810.00
ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Rs. 1,483.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

We don't carry yet: LCD (generic / unspecified)
BeagleBone / Jetson / other SBC
SD / MicroSD Card
Laser / LiDAR / ToF Sensor

Browse all components

How It Works

The Raspberry Pi based Lane Detection System integrates computer vision and active depth sensing to replicate core Advanced Driver Assistance Systems (ADAS) functionalities. At the heart of the system, a single-board computer such as a Raspberry Pi 5 or an NVIDIA Jetson Orin Nano serves as the central processing unit, running a Linux environment with Python 3. Visual data is captured continuously by a connected webcam or Raspberry Pi camera module, pointing forward to view the road ahead. This video feed is processed in real-time using OpenCV and NumPy libraries; the software applies color thresholding, region of interest masking, and the Hough Transform to identify lane markings and calculate the vehicle's position relative to them. Simultaneously, a LIDAR sensor actively sweeps the forward environment, emitting laser pulses to measure distances and generate spatial point cloud data for obstacle detection. By processing both the camera's visual frames and the LIDAR's depth measurements concurrently, the system can cross-reference visible lane boundaries with physical proximity hazards. All operational metrics, camera streams, and diagnostic overlays are output directly to a 7-inch HDMI display, allowing engineers and students to monitor system performance in real-time.

Why Build This

Gain practical undergraduate-level experience designing real-world Advanced Driver Assistance Systems (ADAS) and autonomous vehicle safety logic.

Learn how to integrate and synchronize heterogeneous sensor data, combining optical computer vision inputs from OpenCV with active laser-based LIDAR depth measurements.

Implement high-performance image processing algorithms in Python using NumPy and OpenCV on resource-constrained single-board computers like the Raspberry Pi or NVIDIA Jetson.

Real-World Application

Advanced Driver Assistance Systems (ADAS) for automotive safety, autonomous vehicles, and real-time lane departure warning systems.

Skills You'll Learn

Computer vision programming

image filtering and edge detection

sensor fusion

Python data manipulation with NumPy

embedded Linux environment configuration

hardware power management

Safety Precautions

Ensure proper power regulation (5V, 4A output from the vehicle or isolated 5V 2A USB power during bench testing) to prevent damage to the single board computer and display.

Technology Tags

RASPBERRY PI

NVIDIA JETSON

LIDAR

OPENCV

PYTHON

IMAGE PROCESSING

ADAS

COMPUTER VISION

Frequently Asked Questions

Can I use either a Raspberry Pi or an NVIDIA Jetson for this project?

Yes, the system is designed to run on either platform, though an NVIDIA Jetson Orin Nano may offer enhanced performance for heavy computer vision and machine learning tasks.

What is the primary role of the LIDAR sensor in a lane detection project?

While the camera handles visual lane boundary identification via OpenCV, the LIDAR sensor complements it by providing active depth sensing and precise distance measurements for obstacle detection.

How is power supplied to the single-board computer during testing?

It requires proper power regulation, such as an isolated 5V 2A USB power supply during bench testing, or a regulated 5V 4A output when deployed in an automotive environment.

Gallery

This page is part of Kitkraft's Project Hub — a curated collection of electronics & robotics projects gathered from the maker community to help students and hobbyists discover and learn to build. Kitkraft does not claim authorship of the original project, and makes no representation as to the accuracy, completeness or safety of third-party source material. The summary and "How It Works" explanation above are AI-generated, rewritten with reference to the original tutorial — not a reproduction of it. Full credit and a direct link to the original source are provided above. If you're the original creator and have a concern about how your work is represented here, please contact us and we'll address it promptly.

Kitkraft Project Hub — curated from the maker community, credited at the source.

PRJ-00791