Abstract
Adaptive Front-light System (AFS) is attracting more and more attentions, and plays an important role in road security improvement. This paper firstly introduces the AFS system structure and vehicle dynamics, and then presents a new hybrid multisensory data fusion framework based on neural network and Kalman filter to monitor the status of vehicle and send control signal out. The simulation shows the fusion algorithm can effectively filter the disturbance and provide the optimal signal to actuator.
| Original language | English |
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| Title of host publication | Proceedings of the 29th Chinese Control Conference, CCC'10 |
| Pages | 4847-4850 |
| Number of pages | 4 |
| Publication status | Published - 22 Dec 2010 |
| Event | 29th Chinese Control Conference, CCC'10 - Beijing, China Duration: 29 Jul 2010 → 31 Jul 2010 |
Conference
| Conference | 29th Chinese Control Conference, CCC'10 |
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| Country/Territory | China |
| City | Beijing |
| Period | 29/07/10 → 31/07/10 |
Keywords
- Adaptive Front-light System neural network
- Data fusion
- Kalman filter
- Multi-sensor
ASJC Scopus subject areas
- Control and Systems Engineering