Autonomous Vehicle Technology
A student-led initiative developing autonomous driving algorithms. Explore our research, system architectures, and simulation results for modern vehicle intelligence.
SYS-01 // DATA
LiDAR & Radar
Sensor Fusion
AI_NAV_v09
Neural Logic
Path Planning
DEV_3_PHASE_3
System Testing
Project Phase
Sensor Fusion Analysis
Autonomous driving logic built with technical rigor.
Quantifiable engineering data demonstrating model accuracy, sensor processing rates, and hardware throughput for our BCA project.
Model Accuracy
Precision in semantic segmentation and object detection across diverse urban environments.
Sensor Processing
Real-time LiDAR and camera data fusion throughput for low-latency decision making.
Simulated Miles
Extensive testing in virtual environments to validate path planning and safety logic.
System Latency
Optimized inference cycles ensuring rapid response times for autonomous maneuvers.
Explore our full research findings.
Review our system architecture, simulation videos, and code repositories.
Autonomous driving tech
Technical breakdown of our perception, sensor fusion, and path planning subsystems.
Computer vision
Deep learning models for real-time object detection, lane tracking, and semantic segmentation of road environments.
Sensor fusion
Integrating LiDAR point clouds with camera data to build accurate 3D spatial maps for obstacle avoidance.
Path planning
Autonomous trajectory generation and vehicle control logic for safe navigation in dynamic traffic scenarios.
Engineering Pipeline
A modular technical framework translating raw sensor telemetry into precise autonomous vehicle control decisions.
Capture raw sensor streams from LiDAR, radar, and high-resolution cameras to build a real-time environmental map.
Deploy deep learning models to identify objects, segment road surfaces, and predict dynamic agent trajectories.
Calculate optimal steering, throttle, and braking commands to navigate the vehicle safely through complex traffic.
Engineering the Future of Autonomous Driving
A chronological look at our BCA project journey, from foundational research to complex sensor fusion and real-time simulation testing.
Developing core sensor fusion pipelines for real-time obstacle detection, integrating LiDAR point-cloud data with camera-based semantic segmentation.
Reduced latency in object detection by 35% through optimized ROS2 node communication and hardware-accelerated inference.
Building high-fidelity simulation environments using CARLA to test path-planning algorithms under diverse weather and traffic conditions.
Established a robust testing framework that identified critical edge-case failures in emergency braking logic before physical deployment.
Initial research into self-driving fundamentals, focusing on computer vision basics, lane-keeping algorithms, and sensor data preprocessing.
Successfully implemented a baseline lane-detection model that served as the foundation for all subsequent autonomous navigation modules.
Interested in our technical approach?
Explore our full system architecture, research papers, and simulation results to see how we are solving autonomous navigation challenges.
EXPLORE THE CODE. HELP US DRIVE AUTONOMY.
Join our BCA project initiative. Review our system architecture, experiment with model weights, and help refine our self-driving algorithms.