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Using Eye Movements To Evaluate Effects of Driver Age on Risk Perception in a Driving Simulator Anuj Kumar Pradhan, Kim R. Hammel, Rosa DeRamus, Alexander.

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Presentation on theme: "Using Eye Movements To Evaluate Effects of Driver Age on Risk Perception in a Driving Simulator Anuj Kumar Pradhan, Kim R. Hammel, Rosa DeRamus, Alexander."— Presentation transcript:

1 Using Eye Movements To Evaluate Effects of Driver Age on Risk Perception in a Driving Simulator Anuj Kumar Pradhan, Kim R. Hammel, Rosa DeRamus, Alexander Pollatsek, David A. Noyce, and Donald L. Fisher

2 Introduction  在美國,新手發生事故的比率遠大於一般 駕駛者 (8-9 倍 ) 。  The most common were failures to search ahead, to the side, and to the rear, which together were implicated in 42.7% of the crashes; failure to pay attention (23.0%); and failure to adjust the vehicle’s speed appropriately (20.8%).

3  This study hypothesized novice drivers scanning less widely than more experienced drivers.  試著利用 eye-movement tracker and driving simulator 來評估對駕駛經驗和年紀 的影響。

4 Method  創造出 16 種 risky scenarios ,利用駕駛模 擬器來記錄其相關駕駛行為,並用 eye tracker 來紀錄是否有注意到潛在的危險因 子。  上述 16 種 risky scenarios 並不會真的發生 危險的狀況,以免受測者受到影響。

5 Participants  A total of 72 participants were run in the experiment 1.24novice drivers in their first6 months of driving experience, all 16 or 17 years old; 2.24 younger drivers, 19–29 years old; 3.24 older drivers, 60–75 years old.

6 Apparatus  Driving simulator  Eye tracker

7 Scenarios  All 16 scenarios (S1–S16) that were developed can be viewed on the web (http://www.ecs.umass.edu/hpl/LinkVideo s.htm) http://www.ecs.umass.edu/hpl/LinkVideo s.htmhttp://www.ecs.umass.edu/hpl/LinkVideo s.htm  But only 14 scenarios are described.

8  S1: Truck right turn with walk signal

9  S2: Bicycle on left side of road

10  S3: Truck and driver left turn

11  S4: Signal ahead over hill  S5: T intersection

12  S6: Hidden crosswalk

13  S7: Vehicle on right at intersection

14  S8: Bicycle on right side of road  S9: Driver left turn with truck in opposing lane

15  S10: Pedestrian on left

16  S11: Curved stop ahead

17  S12:Truck blocking travel in lane

18  S13: Truck blocking pedestrian crosswalk

19  S14:Truck left turn and driver straight through intersection

20 Design  Each participant drove through four experimental blocks, each of which contained 4 risky scenario.  The order of the blocks was counterbalanced for each of the three groups.

21 Procedure  The participants were asked to fill out an informed consent form.  They were to follow the lead vehicle throughout the drive and to maintain a 3- s following distance.  The participants drove through the four blocks during which eye movement data and other vehicle data were recorded.

22 Result-ANOVA  the novice drivers engaged in behaviors indicative of their recognition of the potential for risk 35.1% of the time, the younger drivers engaged in such behaviors 50.3% of the time, and the older drivers engaged in such behaviors 66.2% of the time. The effect of age was significant.

23  Whether the groups are relatively clustered Result-Box Plot

24  The types of scenarios in which the novice drivers performed worse than the older drivers.  As is clear from Table 1, on all of the 16 measures of risk perception, novice drivers performed more poorly than older drivers. Result-Individual scenarios

25

26 Discuss-implications  新手駕駛的確需要加以訓練,才能增加到 路安全。  從表 1 ,發現各個情況下的差異不盡相同, 因此訓練時需針對各個情況訓練。  可以藉由這研究來評估訓練機制的好壞。

27  所得結果是否符合田野資料。  利用駕駛模擬器與眼動儀是否與現實一致。  眼動儀的資料不能完全的診斷駕駛行為。 Discuss-limitations


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