The Effects of Warning Lead Time on Situation Awareness in Connected Vehicle Systems

Author(s):  
Xiaomei Tan ◽  
Yiqi Zhang
Author(s):  
Yiqi Zhang ◽  
Changxu Wu ◽  
Chunming Qiao ◽  
Yunfei Hou

The connected vehicle systems (CVS) aim to provide drivers with information in a timely and reliable way to improve transportation safety. With the emerging wireless communication technologies, the vehicles will be equipped with the ability to communicate with each other about the surrounding traffic situations by exchanging vehicle status and motion data via Dedicated Short-Range Communications (DSRC) network (Kenney, 2011). With the assistance of the cooperative collision warnings, the impact of designed warning parameters on driver performance is increasingly important. Existing empirical studies have studied the warning timing and warning reliability in determining the effectiveness of the collision warning systems in advanced driver assistance systems (ADAS). In terms of warning timing, the studies in reached consistent conclusions that early warnings induced more timely braking and longer braking process, resulted in higher trust of the warning systems, and reduced collision rates (for example, Abe & Richardson, 2006a; Lee, McGehee, Brown, & Reyes, 2002; Yan, Xue, Ma, & Xu, 2014; Yan, Zhang, & Ma, 2015; Wan, Wu, and Zhang, 2016). In terms of the warning reliability, research has shown that warnings with a higher reliability increased driver’s trust of the warning systems, led to higher frequency in warning responses, and reduced crash rates (for example, Abe, Itoh, & Yamamura, 2009; Bliss & Acton, 2003; Maltz & Shinar, 2007; Sullivan, Tsimhoni, & Bogard, 2008). However, the interaction effects of warning lead time and warning reliability on driver performance was not examined especially under the connected vehicle settings. The current research investigated the interaction effects of warning lead time (2.5s vs. 4.5s), warning reliability (73% vs. 89%), and speech warning style (command vs. notification) on driver performance and subjective evaluation of warnings in CVS. A driving simulator study with thirty-two participants was conducted to simulate a connected vehicle environment with missing warnings due to the failures in the data transmission within the communication network of the CVS. The results showed command warnings led to a smaller collision rate compared to notification warnings with the warning lead time of 2.5s, whereas notification warnings resulted in a smaller collision rate compared to command warning with the warning lead time of 4.5s. These results suggested notification warnings should be selected when warning lead time is longer and warning reliability is higher, which resulted in higher safety benefits and higher subjective ratings. Command warnings could be selected when warning lead time is shorter since they led to more safety benefits. However, such selection has to be made with caution since command warnings may limit drivers’ response type and were perceived as less helpful than notification warnings.


Author(s):  
Yiqi Zhang ◽  
Changxu Wu

Deaths and injuries resulted from traffic accidents is still a major public health problem. Recent advances in connected vehicle technology support a connected driving environment in which vehicles are enabled to communicate with each other and with roadside infrastructures via Dedicated Short Range Communication (DSRC). Connected vehicle safety applications supported by this technology allow drivers to learn about the traffic situations out of their sight and ahead of time so that drivers are warned early enough to make proper responses. As the connected vehicle systems (CVS) are designed with an aim to improving driver safety, the effectiveness of the CVS can not be achieved without drivers making proper responses in responding to the wireless warnings. Therefore, it is essential to understand and model the mechanism for human processing and responding to warnings from connected vehicle systems, and apply the driver model to optimize the design the CVS at the interface level and the communication level. Queuing Network-Model Human Processor (QN-MHP) is a computational framework that integrates three discrete serial stages of human information processing (i.e., perceptual, cognitive, and motor processing) into three continuous subnetworks. Each subnetwork is constructed of multiple servers and links among these servers. Each individual server is an abstraction of a brain area with specific functions, and links among servers represent neural pathways among functional brain areas. The neurological processing of stimuli is illustrated in the transformation of entities passing through routes in QN-MHP. Since this architecture was established, QN-MHP has been applied to quantify various aspects of aspects of driver behavior and performance, including speed control (Bi & Liu, 2009; Zhao & Wu, 2013b), lateral control (Bi et al., 2012; Bi et al., 2013), driver distraction (Bi et al., 2012; Fuller, Reed & Liu, 2012; Liu, Feyen & Tsimhoni, 2006), and driver workload (Wu & Liu, 2007; Wu et al., 2008). Most of the driver model built upon QN-MHP focused on the modeling of driver performance in normal driving situation. In a previous work of authors, a mathematical model was developed to predict the effects of warning loudness, word choice, and lead time on drivers’ warning reaction time (Zhang, Wu, & Wan, 2016). The current research focused on the development of a mathematical model based on QN-MHP to quantify and predict driver performance in responding to warnings from connected vehicle systems, including warning response time and the selection of warning response type. The model also quantified the effects of important warning characteristics in connected vehicle systems, including warning reliability, warning lead time, and speech warning style. The model was validated via an experimental study indicating its good predictability of driver behavior and performance in connected vehicle systems. In particular, the model was able to explain 68.83% of the warning response type in the initial trial of the experiment with a root mean square error (RMSE) of 0.18. By adding the warning effect on the probability of a response type through trials, the model was able to explain 65.13% of the warning response type in the initial trial of the experiment with a root mean square error (RMSE) of 0.16. In terms of warning response time, the model prediction of warning response time under different warning reliability, style and lead time were very similar to the response time results from the experiments. The model was able to explain 88.30% of the experimental response time in average with a root mean square error (RMSE) of 0.16s. The developed driver model could be applied to optimize the design of the connected vehicle systems based on driver


2019 ◽  
Vol 6 (2) ◽  
pp. 2626-2636 ◽  
Author(s):  
Zhe Yang ◽  
Kuan Zhang ◽  
Lei Lei ◽  
Kan Zheng

2015 ◽  
Vol 7 (2) ◽  
pp. 180
Author(s):  
Lu Pu ◽  
Xiaowei Xu ◽  
Han He ◽  
Hanqing Zhou ◽  
Zhijun Qiu ◽  
...  

Author(s):  
Linjun Zhang ◽  
Gábor Orosz

In this paper, we investigate the nonlinear dynamics of connected vehicle systems. Vehicle-to-vehicle (V2V) communication is exploited when controlling the longitudinal motion of a few vehicles in the traffic flow. In order to achieve the desired system-level behavior, the plant stability and the head-to-tail string stability are characterized at the nonlinear level using Lyapunov functions. A motif-based approach is utilized that allows modular design for large-scale vehicle networks. Stability analysis of motifs are summarized using stability diagrams, which are validated by numerical simulations.


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