Self-driving Car

Project Overview

Project Name: Self Driving Car – AI-Based Autonomous Vehicle System
Duration: 8-12 weeks

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Description

This project aims to design and develop a self-driving car system using artificial intelligence, computer vision, and deep learning. The system enables a vehicle to navigate autonomously in a simulated or real-world environment, understanding its surroundings, following road rules, and making decisions like a human driver. Students will implement key technologies such as object detection, lane tracking, traffic sign recognition, path planning, and real-time control systems.

Learning Objectives
By completing this project, students will
  • Understand the core principles of autonomous driving
  • Learn to integrate computer vision for lane and obstacle detection
  • Implement traffic sign and signal recognition using deep learning
  • Apply decision-making algorithms for driving control
  • Build real-time control systems for simulated or physical vehicles
  • Understand sensor fusion techniques using LiDAR, GPS, and cameras
  • Explore reinforcement learning for self-learning vehicle behavior
  • Gain experience with driving simulators and embedded programming

Project Scope and Features

1

Lane Detection and Following System

  • Real-time lane marking detection using image processing
  • Canny edge detection and Hough transform
  • Curve fitting and road curvature estimation
  • Dynamic lane centering logic

Object and Obstacle Detection

  • Real-time detection of vehicles, pedestrians, and roadblocks
  • Bounding box classification using YOLO or SSD models
  • Distance estimation and collision warning system
  • Braking and lane switching automation
2
3

Traffic Sign and Signal Recognition

  • Detection of stop, speed limit, yield, and turn signs
  • Signal light recognition using color filtering and CNNs
  • Decision-making based on detected signs and signals

Path Planning and Navigation

  • Waypoint-based navigation system
  • Trajectory generation and optimization
  • Static and dynamic path adjustment based on surroundings
  • PID controller integration for smooth turns and acceleration
4
5

Sensor Fusion and Environmental Awareness

  • Integration of GPS for location tracking
  • Use of simulated LiDAR and camera data fusion
  • Environmental mapping and SLAM (Simultaneous Localization and Mapping)

Technical Framework Requirements

Core Technologies and Concepts

  • Computer Vision: OpenCV, TensorFlow/Keras, MediaPipe
  • Object Detection Models: YOLOv5, MobileNet-SSD
  • Path Planning Algorithms: Dijkstra, A*, RRT (optional)
  • Control Systems: PID controllers, motion planning logic
  • Embedded Platforms (optional): Raspberry Pi, NVIDIA Jetson Nano
  • Simulation Environments: CARLA, Gazebo, Udacity simulator
  • Sensor Input: LiDAR (simulated), GPS, ultrasonic (optional)
  • Data Processing: Numpy, Pandas, Matplotlib for performance metrics

System Architecture Components

  • Vision module for lane and object recognition
  • Decision- making engine(rules + ML model based)
  • Path planner for trajectory management
  • Vehicle control module(throttle, steering, brake)
  • Sensor fusion engine and environment mapper
  • Optional dashboard interface for live monitoring

Phase-wise Implementation Strategy

Phase 1: Research and System Design (Week 1–2)

1
Conceptual Foundations
  • Understand levels of driving automation (L0–L5)
  • Analyze architecture of real-world autonomous vehicles
  • Study driving simulators and datasets
Design Planning
  • Define sensor requirements and software stack
  • Plan module-wise responsibilities and input/output

Phase 2: Lane Detection and Vehicle Control (Week 3–4)

1
Vision Pipeline for Lane Detection
  • ROI (Region of Interest) extraction
  • Grayscale and Gaussian blur preprocessing
  • Lane curve estimation using Hough lines
Control System Integration
  • Implement PID controller for steering
  • Adjust vehicle speed based on road curvature
  • Initial tests in simulation environment

Phase 3: Object Detection and Traffic Sign Recognition (Week 5–6)

1
Object Detection
  • Implement YOLOv5/SSD model for obstacle detection
  • Annotate and use datasets for vehicle and pedestrian detection
  • Distance estimation from camera input
Traffic Sign Detection
  • CNN model training on German Traffic Sign Dataset (GTSRB)
  • Classify signs like Stop, Turn Left/Right, Speed Limit
  • Connect recognition to driving decisions

Phase 4: Path Planning and Navigation (Week 7–8)

1
Trajectory Planning
  • Implement waypoint system with adjustable turns
  • Use A* or Dijkstra for obstacle-avoiding routing
  • Integrate path smoothing for sharp turns
Autonomous Navigation in Simulated Environment
  • Run trials in CARLA/Udacity Simulator
  • Adjust path in real time based on vision inputs

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Our Locations

Come and chat with us about your goals over a cup of coffee.

Hyderabad, Telangana

2nd Floor, Hitech City Rd, Above Domino's, opp. Cyber Towers, Jai Hind Enclave, Hyderabad, Telangana.

Bengaluru, Karnataka

3rd Floor, Site No 1&2 Saroj Square, Whitefield Main Road, Munnekollal Village Post, Marathahalli, Bengaluru, Karnataka.