B2 / 自動駕駛系統的感測器融合與追蹤設計
Sensor Fusion and Tracking for Automated Driving

Abhijit Bhattacharjee / MathWorks Inc.


  • 建立多目標追蹤器與感測器融合過濾器
  • 產生動作和感測器模型
  • 針對真實及綜合的資料來設計資料關聯演算法
  • 定義並匯入駕駛情境和路徑來進行模擬
  • 針對雷達和攝影機產生綜合性的偵測資料,再配合GPS/IMU等感測器進行車輛的定位
  • 透過標準化的利基比較、各種可能情境組合、生動的圖表來評估系統的準確性及性能表現

In this session, you will learn to design, simulate, and analyze systems that fuse data from multiple sensors to maintain position, orientation, and situational awareness for automated driving/autonomous systems. Through several examples, we will:

  • Create multi-object trackers and sensor fusion filters
  • Generate motion and sensor models
  • Design data association algorithms for real and synthetic data
  • Define and import scenarios and trajectories for simulation
  • Generate synthetic detection data for radar and camera sensors, along with GPS/IMU sensors for localization
  • Evaluate system accuracy and performance with standard benchmarks, metrics, and animated plots