Towards learning adaptive workload maps

被引：0
|
作者：
Schroedl, S [1 ]
机构：
[1] DaimlerChrysler Res & Technol Ctr, Palo Alto, CA 94304 USA
来源：
IEEE IV2003: INTELLIGENT VEHICLES SYMPOSIUM, PROCEEDINGS | 2003年
关键词：
D O I：
10.1109/IVS.2003.1212985
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
One approach to mitigate the risks of driver distraction is to build an in-vehicle service manager component that is aware of the attentional requirements of the current and of upcoming traffic situations. This component Will rely on technologies for personalized driver workload prediction, based on an enhanced digital map, and/or on sensors for physiological and behavioral workload correlates. In this report, we address first results of our approach towards the following questions: According to our experiments,what method is best for online/predictive workload. estimation? Which sensors are most suitable? How do physiological measurements and subjective rating correlate? Which proportion of workload can be statically predicted (based on map features alone)? How do workload patterns differ between drivers? How dynamic is workload (how long does an influence persist)? Which factors (percentage) influence workload?
引用
收藏
页码：627 / 632
页数：6
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