Quantifying effects of reverse linear perspective as a visual cue on vehicle and platoon crash risk variations in car-following using path analysis

Using multicolor perceptual markings as a rear-end crash risk mitigator: A field investigation

Perceptual markings on roadways are prevailing countermeasures with substantial effectiveness for accident prevention, and a variety of alternatives and derivatives of them are developed to expect to receive an augmented performance of behavioral intervention and crash risk mitigation. However, the proper use of colors as a way of developing effective and innovative perceptual markings is seldomly recognized in-depth from the perspective of visual perceptual mechanism in behind. Given this, in this study, we introduced a kind of multicolor perceptual markings (MCPMs) pattern, i.e., one red marking follows one yellow marking ("1Y + 1R"), two red markings follow two yellow markings ("2Y + 2R"), and three red markings follow three yellow markings ("3Y + 3R"), and evaluated their effects on longitudinal and lateral driving behaviors and real-time safety benefits in car-following via a series of field investigation on a real-world expressway in China. The statistical analyses of the relative differences of speed (θ_v), distance headway (θ_d), time headway (θ_h), lateral movement (θ_p), and crash risk (η_mTTC and η_DRAC, developed from time-to-collision (TTC) and deceleration rate to avoid crash (DRAC)) suggest that, 1) the MCPMs could lead to substantial increases in car-following time and distance headways, and reduction in speed. The maximum time headway increase (0.61 s), speed reduction (1.42 m/s), and distance increase (3.6 m) were found in the condition of "1Y + 1R" compared with the baseline; 2) the MCPMs stabilized the lateral movement of vehicles on the lane at each observation section, and "1Y + 1R" yielded the best performance of lane-keeping; 3) the MCPMs yielded applaudable real-time safety benefits, which were believed to afford the drivers a better chance to accommodate their behaviors to a safer car-following status. The findings of this study suggest the MCPMs could be an especially applaudable form of perceptual markings, and could also be a critical reference of how to use colors in a better way for developing augmented perceptual markings.

Quantitative Study on Road Traffic Environment Complexity under Car-Following Condition

With the development of the drive of electronic communication technology, the driving assistance system that perceives the external traffic environment has developed rapidly. However, when quantifying the complexity of the road traffic environment without fully considering the driving characteristics and subjective feelings, the false alarm rate of the driving warning system increases and affects the early warning effect. In order to more accurately quantify the complexity of the road traffic environment, we analyzed the impact of road traffic environment changes on drivers under the condition of car-following. Firstly, we selected the influencing factors of the traffic environment complexity, such as the driving operation indicators, the vehicle driving status indicators and the road environmental indicators. The weight calculation model of each influence factor is established based on the principal component analysis method. Secondly, the driver’s reaction time during car-following is used as the quantitative index of road traffic environment complexity. The quantitative model of road traffic environment complexity is constructed combined with the weight of road traffic environment complexity. Finally, the driving simulation experiment is designed to verify the complexity quantification model of the road traffic environment. The road traffic environment complexity value calculated in our study is better than the TTC, and the early-warning threshold is raised by 2–5%. The research conclusion can provide a basis for the design of the car alarm system.

The Effects of Dynamic Complexity on Drivers' Secondary Task Scanning Behavior under a Car-Following Scenario

The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.