00778974

Component

https://doi.org/10.3141/1679-18

http://scholar.google.com...+Liu&publication_year=1999

The business plan for the Intelligent Vehicle Initiative (IVI) from the U.S. Department of Transportation contains several candidate services that address collision warning and collision avoidance. Recently, a proactive crash mitigation system that would enhance the crash avoidance and survivability components of IVI has been proposed. An accurate object detection and recognition system is a prerequisite for the proactive crash mitigation system. A vision-based approach to the detection and recognition of vehicles, calculation of their motion parameters, and tracking of multiple vehicles by using a sequence of gray-scale images taken from a moving vehicle is presented. The vision-based system consists of four models: the object detection model, the object recognition model, the object information model, and the object tracking model. Object detection, recognition, and tracking are accomplished by combining the analysis of a single image frame with the analysis of consecutive image frames. In the analysis of the single image frame, the system detects the potential objects by using their shape features and recognizes the objects by using a neural network. Once the objects are recognized, they are tracked in the consecutive image frames by processing only the pertinent areas given by previous frames. The analysis of the single image frame is performed every 10 image frames. The information model will judge whether the objects are hazardous to the host vehicle by using two parameters: time to collision and its time derivative. Experimental results demonstrated a robust system in real-time vehicle recognition, vehicle tracking, and vehicle motion analysis over thousands of image frames.

This paper appears in Transportation Research Record No. 1679, Intelligent Transportation Systems, Vehicle-Highway Automation, and Artificial Intelligence.

English

p. 130-138

1999

Transportation Research Record

0309071054

Figures (9) ; References (10)

Computer vision; Crash avoidance systems; Intelligent vehicles; Motion; Neural networks; Pattern recognition systems; Proximity detectors; Tracking systems

Intelligent Vehicle Initiative

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Dec 1 1999 12:00AM

• ANALYSIS OF INTERNATIONAL BORDER CLEARANCE TECHNOLOGIES
• ARTIFICIAL INTELLIGENCE SEARCH ALGORITHMS FOR DYNAMIC TRAFFIC ROUTING
• BREAKING THE HIGHWAY CAPACITY BARRIER: ADAPTIVE CRUISE CONTROL-BASED CONCEPT
• COMPUTATIONAL ISSUES IN MICRO-GENETIC ALGORITHMS FOR TRAFFIC MANAGEMENT
• DEVELOPMENT OF HIERARCHICAL METHODOLOGY FOR BENEFIT EVALUATION OF VEHICLE-HIGHWAY AUTOMATION: CASE STUDY OF THE HOUSTON KATY FREEWAY
• IMPACT OF INTELLIGENT CRUISE CONTROL ON MOTORWAY CAPACITY
• INCORPORATING NEURAL NETWORK TRAFFIC PREDICTION INTO FREEWAY INCIDENT DETECTION
• INTELLECTUAL CHALLENGES TO DEPLOYABILITY OF ADVANCED VEHICLE CONTROL AND SAFETY SYSTEMS
• INTELLIGENT TRANSPORTATION SYSTEMS PLANNING IN A MEDIUM-SIZED, RELATIVELY UNCONGESTED URBAN AREA
• INTELLIGENT TRANSPORTATION SYSTEMS: A TWO-EDGED SWORD FOR OLDER DRIVERS?
• LEARNING TO ASSOCIATE OBSERVED DRIVER BEHAVIOR WITH TRAFFIC CONTROLS
• LESSONS LEARNED FROM A REVIEW OF EVALUATIONS OF AUTOMATED HIGHWAY SYSTEMS
• METHOD TO PREPROCESS OBSERVED TRAFFIC DATA FOR CONSISTENCY: APPLICATION OF FUZZY OPTIMIZATION CONCEPT
• METROPOLITAN MODEL DEPLOYMENT INITIATIVE: A LEARNING EXPERIENCE
• REGIONAL ARCHITECTURES: STRENGTHENING THE TRANSPORTATION PLANNING PROCESS
• TOWARD A CONSUMER DEMAND-DRIVEN INTELLIGENT TRANSPORTATION SYSTEM POLICY: FINDINGS FROM SOUTHERN CALIFORNIA
• TRAFFIC ESTIMATION FOR PROACTIVE FREEWAY TRAFFIC CONTROL
• TRANSPORTATION OPERATIONS COORDINATING COMMITTEE SYSTEM FOR MANAGING INCIDENTS AND TRAFFIC: EVALUATION OF THE INCIDENT DETECTION SYSTEM
• UNDERSTANDING THE TRANSPORTATION POLICY PROCESS: INTELLIGENT TRANSPORTATION SYSTEMS THROUGH TWO CONCEPTUAL LENSES
• WHAT ADVANCED TRAVELER INFORMATION SYSTEM INFORMATION DO USERS WANT? EVIDENCE FROM IN-VEHICLE NAVIGATION DEVICE USERS