Vehicle Detection, Tracking and Behavior Analysis in Urban Driving Environments Using Road Context

We present a real-time vehicle detection and tracking system to accomplish the complex task of driving behavior analysis in urban environments. We propose a robust fusion system that combines a monocular camera and a 2D Lidar. This system takes advantage of three key components: robust vehicle detection using deep learning techniques, high precision range estimation from Lidar, and road context from the prior map knowledge. The camera and Lidar sensor fusion, data association and track management are all performed in the global map coordinate system by taking into account the sensors' characteristics. Lastly, behavior reasoning is performed by examining the tracked vehicle states in the lane coordinate system in which the road context is encoded. We validated our approach by tracking a leading vehicle while it performed usual urban driving behaviors such as lane keeping, stop-and-go at intersections, lane changing, overtaking and turning. The leading vehicle was tracked consistently throughout the 2. 3 km route and its behavior was classified reliably.

Authors

• Shashwat Verma
• You Hong Eng
• Hai Xun Kong
• Hans Andersen
• Malika Meghjani
• Wei Kang Leong
• Xiaotong Shen
• Chen Zhang 0018
• Marcelo H. Ang
• Daniela Rus

Keywords

• Roads
• Laser radar
• Sensor fusion
• Robot sensing systems
• Vehicle detection
• Estimation
• Autonomous vehicles
• Behavioral Analysis
• Vehicle Track
• Road Context
• Urban Driving
• Detection System
• Coordinate System
• Global System
• Urban Environments
• Tracking System
• Global Map
• Robust Detection
• Vehicle State
• Lane Change
• Global Coordinate System
• Fusion System
• Track Manager
• Point Cloud
• Road Network
• Target Vehicle
• Vehicle Position
• You Only Look Once
• Current Segment
• Adjacent Lane
• Lane Change Maneuver
• Road Structure
• Adjacent Segments
• Line Segment
• Position Estimation