Advanced Driver Assistance Systems Final Year Projects with Source Code

Byadmin Final Year Projects

Advanced Driver Assistance Systems Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Advanced Driver Assistance Systems projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Advanced Driver Assistance Systems Final Year Projects

  1. A Distracted Driving Detection Model Based On Driving Performance
    This project studies how a driver behaves when fully focused and when mentally distracted. Researchers collected driving data from many people using a simulator. They trained a deep learning model to recognize whether a driver is distracted just from the way they drive. The model works very well and can help detect unsafe driving in real time.
  2. A Privacy-Preserving Learning Method for Analyzing HEV Drivers Driving Behaviors
    This project focuses on analyzing how electric and hybrid vehicle drivers behave while driving. Instead of using cameras or GPS that can reveal personal information, it collects data directly from the car’s onboard system. The system uses advanced deep learning models to learn driving patterns and predict risky behavior. When a risky behavior is detected, the car dashboard shows an alert, helping improve safety while keeping driver privacy protected.
  3. A Novel Spatio Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data
    This project focuses on improving driver assistance systems using only GPS data. The researchers developed a method to turn GPS trajectories into images and trained a neural network to analyze them. The system can accurately detect when a car is turning or going straight. It works better than existing methods and helps make driving safer and smarter.
  4. CNN-LSTM Driving Style Classification Model Based on Driver Operation Time Series Data
    This project focuses on recognizing different driving styles accurately and quickly. It collects driver behavior data over time and uses neural networks to identify patterns. The system combines convolutional networks and LSTM to analyze the data and classify driving styles. Tests show it achieves over 93% accuracy with faster processing.
  5. Challenges and Solutions for Service Continuity in Inter-PLMN Handover for Vehicular Applications
    This project focuses on improving mobile network connectivity for connected and automated vehicles. It addresses problems that occur when vehicles switch between network cells or different networks, which can cause service interruptions. The work proposes solutions like using dual modems, adaptive video streaming, and special servers to maintain reliable connections. Tests on 5G networks show these solutions help, but current networks still need improvements for fully reliable vehicle communication.
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Advanced Driver Assistance Systems Project Synopsis & Presentation

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