The unified software development process
The unified software development process
Sensor and Data Fusion Concepts and Applications
Sensor and Data Fusion Concepts and Applications
Bayesian Multiple Target Tracking
Bayesian Multiple Target Tracking
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
Modeling locomotor control: The advantages of mobile gaze
ACM Transactions on Applied Perception (TAP)
Research collaboration and ITS topic evolution: 10 years at T-ITS
IEEE Transactions on Intelligent Transportation Systems
Integrated real-time vision-based preceding vehicle detection in urban roads
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Moving object detection with laser scanners
Journal of Field Robotics
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This paper describes the obstacle detection and tracking algorithms developed for Boss, which is Carnegie Mellon University's winning entry in the 2007 DARPA Urban Challenge. We describe the tracking subsystem and show how it functions in the context of the larger perception system. The tracking subsystem gives the robot the ability to understand complex scenarios of urban driving to safely operate in the proximity of other vehicles. The tracking system fuses sensor data from more than a dozen sensors with additional information about the environment to generate a coherent situational model. A novel multiple-model approach is used to track the objects based on the quality of the sensor data. Finally, the architecture of the tracking subsystem explicitly abstracts each of the levels of processing. The subsystem can easily be extended by adding new sensors and validation algorithms.