Effects of crash warning systems on rear-end crash avoidance behavior under fog conditions

Abstract Reduced visibility conditions increase both the probability of rear-end crash occurrences and their severity. Crash warning systems that employ data from connected vehicles have potential to improve vehicle safety by assisting drivers to be aware of the imminent situations ahead in advance and then taking timely crash avoidance action(s). This study provides a driving simulator study to evaluate the effectiveness of the Head-up Display warning system and the audio warning system on drivers’ crash avoidance performance when the leading vehicle makes an emergency stop under fog conditions. Drivers’ throttle release time, brake transition time, perception response time, brake reaction time, minimum modified time-to-collision, and maximum brake pedal pressure are assessed for the analysis. According to the results, the crash warning system can help decrease drivers’ reaction time and reduce the probability of rear-end crashes. In addition, the effects of fog level and drivers’ characteristics including gender and age are also investigated in this study. The findings of this study are helpful to car manufacturers in designing rear-end crash warning systems that enhance the effectiveness of the system’s application under fog conditions.

[1]  Jing Shi,et al.  Effect Analysis of Intermittent Release Measures in Heavy Fog Weather with an Improved CA Model , 2013 .

[2]  Ming Chen,et al.  Drivers’ rear end collision avoidance behaviors under different levels of situational urgency , 2016 .

[3]  Kaan Ozbay,et al.  Derivation and Validation of New Simulation-Based Surrogate Safety Measure , 2008 .

[4]  A Lie,et al.  Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes. , 2015, Accident; analysis and prevention.

[5]  Andrés Soler,et al.  Efficacy and feeling of a vibrotactile Frontal Collision Warning implemented in a haptic pedal , 2010 .

[6]  Cristy Ho,et al.  Assessing the effectiveness of "intuitive" vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator. , 2006, Accident; analysis and prevention.

[7]  Thomas J Triggs,et al.  Driver distraction: the effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. , 2006, Accident; analysis and prevention.

[8]  Robert E Mann,et al.  Gender differences and demographic influences in perceived concern for driver safety and support for impaired driving countermeasures. , 2012, Journal of safety research.

[9]  C. Spence,et al.  Multisensory warning signals for event perception and safe driving , 2008 .

[10]  Mohamed Abdel-Aty,et al.  Crash risk analysis during fog conditions using real-time traffic data. , 2017, Accident; analysis and prevention.

[11]  Mohamed Abdel-Aty,et al.  Comparative analysis of zonal systems for macro-level crash modeling. , 2017, Journal of safety research.

[12]  Henrik Lind An efficient visual forward collision warning display for vehicles , 2007 .

[13]  Mohamed Abdel-Aty,et al.  Intersection crash prediction modeling with macro-level data from various geographic units. , 2017, Accident; analysis and prevention.

[14]  Stephen Legg,et al.  The effect of cell phone type on drivers subjective workload during concurrent driving and conversing. , 2003, Accident; analysis and prevention.

[15]  Katsuya Matsunaga,et al.  Differences of drivers' reaction times according to age and mental workload. , 2008, Accident; analysis and prevention.

[16]  Hong Z. Tan,et al.  Driver Reaction Time to Tactile and Auditory Rear-End Collision Warnings While Talking on a Cell Phone , 2009, Hum. Factors.

[17]  Lana M Trick,et al.  Driving in fog: the effects of driving experience and visibility on speed compensation and hazard avoidance. , 2012, Accident; analysis and prevention.

[18]  Don Scott,et al.  Car following decisions under three visibility conditions and two speeds tested with a driving simulator. , 2007, Accident; analysis and prevention.

[19]  Nicolas Saunier,et al.  Pedestrian Crosswalk Safety at Nonsignalized Crossings During Nighttime: Use of Thermal Video Data and Surrogate Safety Measures , 2016 .

[20]  Michael E Rakauskas,et al.  Effects of naturalistic cell phone conversations on driving performance. , 2004, Journal of safety research.

[21]  Bing Wu,et al.  Shockwave-based queue estimation approach for undersaturated and oversaturated signalized intersections using multi-source detection data , 2017, J. Intell. Transp. Syst..

[22]  Robert Gray,et al.  A Comparison of Tactile, Visual, and Auditory Warnings for Rear-End Collision Prevention in Simulated Driving , 2008, Hum. Factors.

[23]  Mohamed Abdel-Aty,et al.  Analysis of drivers' behavior under reduced visibility conditions using a Structural Equation Modeling approach , 2011 .

[24]  Shanshan Zhao,et al.  Weather impacts on single-vehicle truck crash injury severity. , 2016, Journal of safety research.

[25]  Yang Liu,et al.  Effects of foggy conditions on drivers’ speed control behaviors at different risk levels , 2014 .

[26]  Daxin Tian,et al.  A rear-end collision avoidance system of connected vehicles , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[27]  Xiaomeng Li,et al.  Effect of auditory in-vehicle warning information on drivers’ brake response time to red-light running vehicles during collision avoidance , 2016 .

[28]  Matthew P. Reed,et al.  Comparison of driving performance on-road and in a low-cost simulator using a concurrent telephone dialling task , 1999 .

[29]  Katharina Dahmen-Zimmer,et al.  Effects of time pressure on left-turn decisions of elderly drivers in a fixed-base driving simulator , 2011 .

[30]  Helai Huang,et al.  Integrating macro- and micro-level safety analyses: a Bayesian approach incorporating spatial interaction , 2019 .

[31]  John Richardson,et al.  Alarm timing, trust and driver expectation for forward collision warning systems. , 2006, Applied ergonomics.

[32]  M. Rizzo,et al.  Driver Performance in the Moments Surrounding a Microsleep. , 2008, Transportation research. Part F, Traffic psychology and behaviour.

[33]  Kristofer D. Kusano,et al.  Age and Gender Differences in Time to Collision at Braking From the 100-Car Naturalistic Driving Study , 2014, Traffic injury prevention.

[34]  Xuedong Yan,et al.  Discrimination of Effects between Directional and Nondirectional Information of Auditory Warning on Driving Behavior , 2015 .

[35]  Mohamed Abdel-Aty,et al.  A study on crashes related to visibility obstruction due to fog and smoke. , 2011, Accident; analysis and prevention.

[36]  Nicolas Saunier,et al.  A novel framework to evaluate pedestrian safety at non-signalized locations. , 2018, Accident; analysis and prevention.

[37]  Michael J. Flannagan,et al.  RISK OF FATAL REAR-END COLLISIONS: IS THERE MORE TO IT THAN ATTENTION? , 2005 .

[38]  Ellen Haas,et al.  Multimodal warnings to enhance risk communication and safety , 2014 .

[39]  Yuting Zhang,et al.  The influence of in-vehicle speech warning timing on drivers' collision avoidance performance at signalized intersections , 2015 .

[40]  Niklas Strand,et al.  Semi-automated versus highly automated driving in critical situations caused by automation failures , 2014 .

[41]  Chong-Cheng Hsu,et al.  The effect of a collision warning system on the driving performance of young drivers at intersections , 2009 .

[42]  David Crundall,et al.  VISUAL SEARCH WHILE DRIVING: SKILL AND AWARENESS DURING INSPECTION OF THE SCENE , 2002 .

[43]  Frank Drews,et al.  A Comparison of the Cell Phone Driver and the Drunk Driver , 2004, Hum. Factors.

[44]  Fabrizio D'Amico,et al.  Applying telecommunications methodology to road safety for rear-end collision avoidance , 2015 .

[45]  Normand Teasdale,et al.  Mental workload when driving in a simulator: effects of age and driving complexity. , 2009, Accident; analysis and prevention.

[46]  Juneyoung Park,et al.  Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data , 2018 .

[47]  Xiaomeng Li,et al.  Effects of fog, driver experience and gender on driving behavior on S-curved road segments. , 2015, Accident; analysis and prevention.

[48]  Matias Viström,et al.  Effects of forward collision warning and repeated event exposure on emergency braking , 2013 .

[49]  Qing Cai,et al.  Shock Wave Approach for Estimating Queue Length at Signalized Intersections by Fusing Data from Point and Mobile Sensors , 2014 .

[50]  Hong Yang,et al.  Estimation of Traffic Conflict Risk for Merging Vehicles on Highway Merge Section , 2011 .

[51]  Trent Victor,et al.  Eye movement and brake reactions to real world brake-capacity forward collision warnings--a naturalistic driving study. , 2013, Accident; analysis and prevention.