Latest revision as of 11:31, 25 January 2021

Abstract

Multisensor data fusion and integration is a rapidly evolving research area that requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. Multisensor data fusion refers to the synergistic combination of sensory data from multiple sensors and related information to provide more reliable and accurate information than could be achieved using a single, independent sensor (Luo et al., 2007). Actually Multisensor data fusion is a multilevel, multifaceted process dealing with automatic detection, association, correlation, estimation, and combination of data from single and multiple information sources. The results of data fusion process help users make decisions in complicated scenarios. Integration of multiple sensor data was originally needed for military applications in ocean surveillance, air-to air and surface-to-air defence, or battlefield intelligence. More recently, multisensor data fusion has also included the nonmilitary fields of remote environmental sensing, medical diagnosis, automated monitoring of equipment, robotics, and automotive systems (Macci et al., 2008). The potential advantages of multisensor fusion and integration are redundancy, complementarity, timeliness, and cost of the information. The integration or fusion of redundant information can reduce overall uncertainty and thus serve to increase the accuracy with which the features are perceived by the system. Multiple sensors providing redundant information can also serve to increase reliability in the case of sensor error or failure. Complementary information from multiple sensors allows features in the environment to be perceived that are impossible to perceive using just the information from each individual sensor operating separately. (Luo et al., 2007) Besides, driving as one of our daily activities is a complex task involving a great amount of interaction between driver and vehicle. Drivers regularly share their attention among operating the vehicle, monitoring traffic and nearby obstacles, and performing secondary tasks such as conversing, adjusting comfort settings (e.g. temperature, radio.) The complexity of the task and uncertainty of the driving environment make driving a very dangerous task, as according to a study in the European member states, there are more than 1,200,000 traffic accidents a year with over 40,000 fatalities. This fact points up the growing demand for automotive safety systems, which aim for a significant contribution to the overall road safety (Tatschke et al., 2006). Therefore, recently, there are an increased number of research activities focusing on the Driver Assistance System (DAS) development in order O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Original document

The different versions of the original document can be found in:

• http://www.intechopen.com/articles/show/title/multisensor_data_fusion_strategies_for_advanced_driver_assistance_systems

• https://www.intechopen.com/citation-pdf-url/6084 under the license cc-by-nc-sa

• https://www.intechopen.com/books/sensor_and_data_fusion/multisensor_data_fusion_strategies_for_advanced_driver_assistance_systems,

https://cdn.intechopen.com/pdfs/6084/InTech-Multisensor_data_fusion_strategies_for_advanced_driver_assistance_systems.pdf,

https://researchspace.auckland.ac.nz/handle/2292/23473,

https://cdn.intechweb.org/pdfs/6084.pdf,

https://researchspace.auckland.ac.nz/bitstream/2292/23473/6/Multisensor%20Data%20Fusion-%20Sensor%20and%20Data%20Fusion%202009withcoversheet.pdf,

https://academic.microsoft.com/#/detail/2150355991

• https://www.intechopen.com/download/pdf/6084,

http://dx.doi.org/10.5772/6575