Attention allocation patterns in naturalistic driving
Introduction
Exploring the causes of motor vehicle crashes has become a pressing issue. The majority of crashes are considered preventable, provided that the surrounding area is properly observed by the driver and adequate maneuvers are successfully executed (Wong et al., 2010). Presumably, misallocating attention is one of the most critical contributing factors in crashes (Brown et al., 2000, Mcknight and Mcknight, 2003, Underwood et al., 2003, Underwood, 2007, Di Stasi et al., 2009, Olson et al., 2009, Chan et al., 2010). It inhibits the driver's ability to perceive information adequately and increases the likelihood of a crash. Thus, understanding the patterns of attention allocation is crucial to analyzing the relationship between crashes and ways to maintain situational awareness through visual transition.
Safe driving requires the driver to pay continued attention to various areas and to constantly update awareness of the driving environment. Locations or objects do not attract drivers’ attention randomly; specific patterns draw a driver's visual field. In general, before implementing maneuvering intentions, drivers tend to look in the direction of future vehicle trajectories, i.e., where they expect the greatest number of threats to occur (Salvucci and Liu, 2002, Underwood et al., 2002, Underwood et al., 2003, Nabatilan, 2007, Underwood, 2007, Levin et al., 2009). For instance, moving forward constitutes a major driving activity. Hence, the frontal area attracts the most attention in almost all driving conditions (Underwood et al., 2003, Nabatilan, 2007, Underwood, 2007, Levin et al., 2009). Changing lanes requires heightened attention to be invested in the adjacent lane (Salvucci and Liu, 2002, Underwood et al., 2003). Entering an intersection compels drivers to look to both sides of the intersected roads (Summala et al., 1996). In addition to the attention required for specific intended maneuvers, drivers allocate attention to surrounding areas to maintain awareness of traffic conditions and to prevent possible conflicts caused by other vehicles (Crundall et al., 2006).
In other words, the key to safe driving is the adequate distribution of the driver's attention both to the forward area and to other non-forward focal points. Shifting attention away from the frontal area invites a possible lack of awareness of traffic conditions ahead and increases the unawareness of safety considerations (Brown et al., 2000). Klauer et al. (2006) stated that shifting vision away from the forward area longer than 2 s increases the crash/near-crash risk by at least twofold. By contrast, focusing only on the frontal area limits the driver's awareness of the surrounding traffic and the time to react to sudden dangers. Knowledge of the patterns in which drivers allocate visual attention between frontal and surrounding areas provides insight into the information-seeking behavior of drivers and its relationship to safety. To investigate these attention allocation patterns, we posed the following four questions: (1) How should the patterns be represented? (2) Do patterns drivers commonly adopt occur? (3) If so, what are the patterns? (4) What factors contribute to the patterns?
Before analyzing the contribution of specific attributes to attention allocation, an appropriate method for representing attention allocation must be identified. Therefore, this study investigated (1) methods for representing attention allocation patterns, and (2) the occurrence of actual representative patterns of the available sample of drivers from the 100-car event database.
Section snippets
Current practiced methods
Driver attention is not a manifest variable that can be measured directly. Thus, developing an appropriate representation of attention allocation is challenging. Nevertheless, various representations have been provided to analyze several aspects of attention allocation.
One method entails representing attention allocation by using a single focal point. The duration and frequency of drivers transiting their visual fields to a specific direction have been intensively studied. The results have
Data
This study used the 100-car naturalistic glance data collected by the Virginia Tech Transportation Institute (VTTI) (Neale et al., 2002, Dingus et al., 2006, Klauer et al., 2006). The released online data included a baseline database and an event database. The baseline database contained only 6 s of glance data in each record, which is insufficient for this analysis. Therefore, this study adopted the event database, which contains 68 crashes and 760 near-crash incidents (VTTI, 2012).
The 100-car
Generated renewal cycles
In total, 2256 renewal cycles with 91 types were generated. The shortest renewal cycles contained only two glances: one forward and one non-forward focal point. The longest cycle contained 12 glances.
As shown in Table 2, the most frequent renewal cycles were 2-glance cycles, with 90.74% of the data falling into this category. A markedly smaller number of cycles were 3-glance, at 7.18%; and 4-glance cycles accounted for only 1.24%. Renewal cycles with 5 or more glances accounted for 0.85% of the
Conclusion
This study proposes the concept of the renewal cycle to analyze the entire process of driver attention allocation to understand the manner in which vision is transited among various focal points. Analyses of renewal cycles enabled identification of the characteristics associated with each focal point and the attention patterns that occur most frequently. Although these sample drivers were not representative, the results were promising and many of our findings offer potential for practical
Acknowledgments
The authors would like to thank the reviewers for their insightful comments and the National Science Council, Taiwan, Republic of China for financially supporting this research (NSC 100-2221-E-009-120-MY3).
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