The Framework of Intelligent Vehicles

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


This chapter proposes an integrated current and future safety situation analysis framework as general as possible, where we model not only sensing phases, but also control phases. In this framework, a speed estimation algorithm based on lidar data is used to distinguish two types of obstacles: static objects and moving objects. On the basis of the speeds and types of obstacles, we form obstacle tracks using only a single sensor, and after that a track fusion approach is used to yield accurate and robust global tracks. Furthermore, we use camera to detect lanes and obstacles in Regions of Interest (ROIs) generated by range sensors, such as vehicles and pedestrians. Finally, combining the lane structure with obstacle tracks, we can model the traffic environment and assess road situation at both the current and near future time.


Vehicle Dynamic Lidar Data Sensor Fusion Adaptive Cruise Control Safe Driving 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Automation EngineeringUniversity of Electronic Science and TechnologyChengduPeople’s Republic of China

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