Multisensor simultaneous vehicle tracking and shape estimation
conference paper
This work focuses on vehicle automation applications that require both the estimation of kinematic and geometric information of surrounding vehicles, e.g., automated overtaking or merging. Rather then using one sensor that is able to estimate a vehicle's geometry from each sensor frame, e.g., a lidar, a multisensor simultaneous vehicle tracking and shape estimation approach is proposed. Advanced measurement models and adequate Bayesian filters enable the shape estimation that is impossible with any of the sensors individually. The use of multiple sensors increases robustness, lowers the complexity of the sensors involved and leads to a gradual loss of performance in case a sensor fails. A series of real world experiments is performed to analyze the performance of the proposed method.
TNO Identifier
572369
ISBN
9781509018215
Publisher
Institute of Electrical and Electronics Engineers Inc.
Article nr.
7535453
Source title
2016 IEEE Intelligent Vehicles Symposium, IV 2016, 19 June 2016 through 22 June 2016
Pages
630-635
Files
To receive the publication files, please send an e-mail request to TNO Repository.