AUTONOMOUS VEHICLE COMPUTER VISION TRAINING SYSTEM

ADVANCED ML ENGINEERING PORTFOLIO PROJECT

DISTRIBUTED TRAINING • REAL-TIME SIMULATION • PRODUCTION DEPLOYMENT

8x
Training Speedup
60 FPS
Simulation Rate
20ms
Inference Latency
82%
Detection mAP
1800+
Requests/sec
4 Tasks
Multi-task Model

DISTRIBUTED TRAINING PROGRESS

Epoch 47/100
Current Progress
8 GPUs
Active Nodes
2.4k
Samples/sec
↑ 12% vs baseline
0.127
Current Loss
↓ 0.023 this epoch

GPU Utilization

GPU0
94%
GPU1
91%
GPU2
89%
GPU3
96%

Memory Usage

VRAM Used 22.4 GB / 24 GB
RAM Used 45.2 GB / 64 GB
Cache Hit Rate 94.7%

Training Metrics Timeline

2.1h
Time Elapsed
1.8h
Est. Remaining
47%
Convergence
0.001
Learning Rate
0.127
Validation Loss
0.89
Training Acc

Convergence Analysis

Loss Decreasing
Steady improvement over last 10 epochs
Learning Active
LR schedule optimizing convergence
Target: 0.1
Current: 0.127 (27% to target)
ETA: 1.8h
Based on current convergence rate

3D SIMULATION ENVIRONMENT

60%
1.0x
24
Active Vehicles
60 FPS
Render Rate
8
Pedestrians
Clear
Weather
2.3km
Map Size
12ms
Latency
Simulation Info
📍 Location: Downtown Intersection
🕐 Time: 14:30 (Day)
🌡️ Temperature: 22°C
🚦 Traffic Lights: Active
AI Status
✅ Perception: Active
✅ Planning: Active
✅ Control: Active
⚠️ Confidence: 94.7%

Vehicle Fleet Status

Vehicle #001 ● Autonomous
Type: Tesla Model 3
Speed: 45.2 km/h
Battery: 87%
Status: Following route
Vehicle #002 ● Manual
Type: BMW iX
Speed: 38.7 km/h
Battery: 92%
Status: Lane changing
Vehicle #003 ● Autonomous
Type: Mercedes EQS
Speed: 52.1 km/h
Battery: 78%
Status: Overtaking
Vehicle #004 ● Emergency
Type: Ambulance
Speed: 65.3 km/h
Fuel: 95%
Status: Emergency response

PERFORMANCE ANALYSIS

32.1 FPS
Average Performance
19.3ms
Average Latency
93.3%
Average Accuracy
1,500
Throughput (ops/s)

SYSTEM ARCHITECTURE

CORE ML ENGINEERING

  • ▶ Distributed training with Ray & Kubernetes
  • ▶ Multi-task learning architecture
  • ▶ Uncertainty-weighted loss functions
  • ▶ Auto-scaling from 1-10 GPU nodes
  • ▶ Production-ready MLOps pipeline
  • ▶ Real-time model serving

ADVANCED GRAPHICS

  • ▶ High-resolution 1920x1080 rendering
  • ▶ Realistic vehicle models & textures
  • ▶ Dynamic shadows & particle effects
  • ▶ Weather simulation (rain, fog, snow)
  • ▶ Interactive 3D visualizations
  • ▶ Professional video demonstrations

PERFORMANCE METRICS

  • ▶ 8x training speedup achieved
  • ▶ 60 FPS real-time simulation
  • ▶ Sub-20ms inference latency
  • ▶ 1800+ requests/sec throughput
  • ▶ 95% GPU utilization efficiency
  • ▶ 4-task multi-modal learning

PRODUCTION FEATURES

  • ▶ Kubernetes orchestration
  • ▶ Auto-scaling infrastructure
  • ▶ Real-time monitoring
  • ▶ A/B testing framework
  • ▶ CI/CD pipeline integration
  • ▶ Multi-cloud deployment

MODEL PERFORMANCE COMPARISON

Production Model
Best Overall Performance
mAP: 0.85 • mIoU: 0.80
Real-Time Model
Fastest Inference
12ms • 68MB
Edge-Optimized
Balanced Performance
15ms • 95MB
Model Evolution
6 Models Trained
+30% Improvement