Experimental research on the effectiveness of navigation prompt messages based on a driving simulator: a case study

In China, F-type-5 m intersections are not uncommon. One approach of these intersections usually includes a driveway closely followed by an intersecting street, and the driveway and the intersecting street are parallel and approximately 5 m apart. Nowadays, drivers often rely on the navigation systems for directions. However, it is found that the navigation systems sometimes mislead or confuse drivers to make wrong turns or miss their turns at such F-type-5 m intersections. This study proposed to employ driving simulation to identify the appropriate prompt message delivered at the right prompt timing to help drivers navigate through such F-type-5 m intersections. First, a within-subjects two-factor experiment was designed. One factor was the Prompt Timing Mode (PTM), representing a set of three sequential messages broadcast by the navigation system at varying distances to the intended intersection; the other factor was the Prompt Message Type (PMT), representing various sets of three sequential messages broadcast by the navigation system. Three Prompt Timing Modes were used: PTM1 = {− 400 m, -200 m, − 30 m}, PTM2 = {− 300 m, − 150 m, − 30 m}, and PTM3 = {− 200 m, − 100 m, − 30 m}. Three Prompt Message Types were defined: PMT-A = {Turn right at the traffic light XXm ahead; Turn right at the traffic light XXm ahead; Turn right}, PMT-B = {Turn right at the traffic light XXm ahead, enter YY street; Turn right at the traffic light XXm ahead, enter YY street; Turn right}, PMT-C = {Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right at the traffic light XXm ahead, enter YY street, and please use the second right turn lane; Turn right}. The combinations of the two factors generated nine experimental intersections which were randomly assigned to three experimental routes. Then, a total of 37 drivers were recruited, and participated in the driving simulation experiment from which vehicle operation data were collected under different prompt timing modes and message types. Next, the repeated Analysis of Variance (rANOVA) was performed to examine the effects of different prompt timing modes and prompt message types on vehicle operation indicators, such as Driving Time, Standard Deviation of Speed, Absolute Value of Acceleration, and Standard Deviation of Acceleration. Finally, the grey near-optimal method was adopted to evaluate the effectiveness of three prompt message types under each prompt timing mode. The rANOVA results showed the vehicle operation in the F-type-5 m intersection was affected by prompt timing modes and prompt message types; the evaluation results indicated that PMT-C made drivers perform better in PTM1 and PTM3, while PMT-B made drivers perform better inPTM2. However, the effectiveness of PMT-A was the lowest in each prompt timing mode. The research results provide valuable guidance to design the human machine interface of navigation systems, which can help drivers safely navigate through F-type-5 m intersections. This research also has laid solid foundations for establishing navigation messaging design guidelines.

[1]  J M Schraagen,et al.  Effects of two types of intra-team feedback on developing a shared mental model in Command & Control teams , 2000, Ergonomics.

[2]  Sheue-Ling Hwang,et al.  EFFECTS ON DRIVING BEHAVIOR OF CONGESTION INFORMATION AND OF SCALE OF IN-VEHICLE NAVIGATION SYSTEMS , 2003 .

[3]  Yuan Wei,et al.  Experiment on perception decision adjustment operation mode of automobile drivers , 2007 .

[4]  Chih-Fu Wu,et al.  A Study on the Design of Voice Navigation of Car Navigation System , 2009, HCI.

[5]  Heetae Kim,et al.  Understanding driver adoption of car navigation systems using the extended technology acceptance model , 2015, Behav. Inf. Technol..

[6]  Guohui Zhang,et al.  A generic approach for examining the effectiveness of traffic control devices in school zones. , 2015, Accident; analysis and prevention.

[7]  Han Ding,et al.  Effects of longitudinal speed reduction markings on left-turn direct connectors. , 2018, Accident; analysis and prevention.

[8]  Wen-Chen Lee,et al.  Comparison of Portable and Onboard Navigation System for the Effects in Real Driving , 2010 .

[9]  Kimihiko Nakano,et al.  Eye-Gaze Tracking Analysis of Driver Behavior While Interacting With Navigation Systems in an Urban Area , 2016, IEEE Transactions on Human-Machine Systems.

[10]  J. Walker,et al.  In-vehicle navigation devices: Effects on the safety of driver performance , 1990, Vehicle Navigation and Information Systems Conference, 1991.

[11]  Raghavan Srinivasan,et al.  Effect of Selected In-Vehicle Route Guidance Systems on Driver Reaction Times , 1997, Hum. Factors.

[12]  Frédéric Vanderhaegen,et al.  A rule-based support system for dissonance discovery and control applied to car driving , 2016, Expert Syst. Appl..

[13]  Daniel Kahneman,et al.  Availability: A heuristic for judging frequency and probability , 1973 .

[14]  Eunil Park,et al.  Driver acceptance of car navigation systems: integration of locational accuracy, processing speed, and service and display quality with technology acceptance model , 2013, Personal and Ubiquitous Computing.

[15]  Han Ding,et al.  Experimental research on the effectiveness and adaptability of speed reduction markings in downhill sections on urban roads: a driving simulation study. , 2015, Accident; analysis and prevention.

[16]  Xiaohua Zhao,et al.  The effect of chevron alignment signs on driver performance on horizontal curves with different roadway geometries. , 2015, Accident; analysis and prevention.

[17]  Frédéric Vanderhaegen Cooperation and learning to increase the autonomy of ADAS , 2011, Cognition, Technology & Work.

[18]  Pei-Chun Chen,et al.  Applying the TAM to travelers' usage intentions of GPS devices , 2011, Expert Syst. Appl..

[19]  Salaheddine Bendak,et al.  The role of roadside advertising signs in distracting drivers. , 2010 .

[20]  W U Yi-Hu,et al.  A Safety Analysis Method for Highway Based on Average Speed , 2008 .

[21]  Akimasa Fujiwara,et al.  Effects of in-vehicle warning information on drivers' decelerating and accelerating behaviors near an arch-shaped intersection. , 2009, Accident; analysis and prevention.

[22]  Tom Stewart Editorial , 2015, Behav. Inf. Technol..

[23]  Georg Jahn,et al.  Peripheral detection as a workload measure in driving: Effects of traffic complexity and route guidance system use in a driving study , 2005 .

[24]  Myounghoon Jeon,et al.  Menu Navigation With In-Vehicle Technologies: Auditory Menu Cues Improve Dual Task Performance, Preference, and Workload , 2015, Int. J. Hum. Comput. Interact..

[25]  Mikael B. Skov,et al.  Studying driver attention and behaviour for three configurations of GPS navigation in real traffic driving , 2010, CHI.

[26]  Wen-Chen Lee,et al.  Effects of using a portable navigation system and paper map in real driving. , 2008, Accident; analysis and prevention.

[27]  Huaguo Zhou,et al.  Evaluation of navigation performances of GPS devices near interchange area pertaining to wrong-way driving , 2016 .

[28]  Jing Zhao,et al.  Impact of in-vehicle navigation information on lane-change behavior in urban expressway diverge segments. , 2017, Accident; analysis and prevention.

[29]  Ming-Hui Wen,et al.  Comparison of head-up display (HUD) vs. head-down display (HDD): driving performance of commercial vehicle operators in Taiwan , 2004, Int. J. Hum. Comput. Stud..

[30]  Gary Burnett,et al.  ‘Turn right at the Traffic Lights’: The Requirement for Landmarks in Vehicle Navigation Systems , 2000, Journal of Navigation.

[31]  Tracy Ross,et al.  An empirical study to determine guidelines for optimum timing of route guidance instructions , 1995 .