Autonomous driving technology relies on a combination of algorithms to perceive the environment, make decisions, and control the vehicle. Key algorithm categories include:
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Perception Algorithms:
- Computer Vision: Detects objects (cars, pedestrians, traffic signs) using CNNs (Convolutional Neural Networks). Example: Lane detection with YOLO (You Only Look Once) or Faster R-CNN.
- LiDAR Processing: Point cloud segmentation and object recognition using algorithms like VoxelNet or PointNet.
- Sensor Fusion: Combines data from cameras, LiDAR, radar, and GPS using Kalman Filters or Particle Filters to improve accuracy.
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Localization Algorithms:
- SLAM (Simultaneous Localization and Mapping): Builds maps while tracking the vehicle's position. Example: Graph-based SLAM or LOAM (Lidar Odometry and Mapping).
- HD Map Matching: Uses high-definition maps and GPS data for precise positioning.
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Path Planning Algorithms:
- Global Path Planning: Finds an optimal route using A* or Dijkstra’s algorithm on a digital map.
- Local Path Planning: Avoids dynamic obstacles with algorithms like Dynamic Window Approach (DWA) or RRT* (Rapidly-exploring Random Tree).
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Control Algorithms:
- PID Control: Adjusts steering, acceleration, and braking based on error signals.
- Model Predictive Control (MPC): Predicts future states to optimize trajectory under constraints.
For scalable computing and AI model training, Tencent Cloud offers services like TI-ONE (AI Platform for model development) and GPU-accelerated instances (e.g., NVIDIA T4/V100) to handle large-scale data processing and real-time inference.