Analyzing Pre-braking Behaviors of Drivers with Varying Experience

Many accidents are caused by drivers abruptly braking, or by their braking-related behaviors. However, it is challenging to collect data on the pre-braking behaviors of drivers by using sensors installed on vehicles, because of which this issue has not been adequately studied. In this paper, we focus on the differences in pre-braking behaviors between experienced and novice drivers by analyzing data collected from a motion capture device attached to them. The results of experiments and the Mann–Whitney U-test show that experienced drivers usually set their feet farther than novice drivers from the braking pad to reduce the chances of pressing it by mistake. However, when they needed to brake, their right foot had a lower response time for pushing the braking pad than novice drivers. The results of this study can provide guidelines for preventing accidents in the development of advanced driver assistance systems and can offer useful insights for training new drivers.

[1]  David Crundall,et al.  What happens next? Predicting other road users' behaviour as a function of driving experience and processing time , 2009, Ergonomics.

[2]  Junqiang Xi,et al.  Learning and Inferring a Driver's Braking Action in Car-Following Scenarios , 2018, IEEE Transactions on Vehicular Technology.

[3]  Liu Junyi,et al.  Based on Binocular Identification Technology of Automobile Active Braking Safety Distance Model Research , 2016, 2016 International Symposium on Computer, Consumer and Control (IS3C).

[4]  Alexander Pollatsek,et al.  Using Eye Movements to Evaluate a PC-Based Risk Awareness and Perception Training Program on a Driving Simulator , 2006, Hum. Factors.

[5]  Birsen Donmez,et al.  Anticipation in Driving: The Role of Experience in the Efficacy of Pre-event Conflict Cues , 2014, IEEE Transactions on Human-Machine Systems.

[6]  Xuehan Ma,et al.  Analysis of Driver Brake Behavior Under Critical Cut-in Scenarios , 2018, 2018 IEEE Intelligent Vehicles Symposium (IV).

[7]  Weixu Wang,et al.  Modeling of the Chinese driver's braking behavior in the simulated traffic scene of rear-end collision avoidance , 2011 .

[8]  Shoji Nishimura,et al.  Culture-Based Color Influence Paths Analysis by Using Eye-Tracking Devices , 2019, 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech).

[9]  Mario Zechner,et al.  Beginning Android Games , 2012, Apress.

[10]  Haipeng Wang,et al.  Braking assistance algorithm considering driver characteristics at signalized intersection , 2017, 2017 4th International Conference on Transportation Information and Safety (ICTIS).

[11]  Nanxiang Li,et al.  Understand driver awareness through brake behavior analysis: Reactive versus intended hard brake , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[12]  Qun Jin,et al.  Experiment Design and Analysis of Cross-Cultural Variation in Color Preferences Using Eye-Tracking , 2019, Lecture Notes in Electrical Engineering.