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801.
Existing driver models mainly account for drivers’ responses to visual cues in manually controlled vehicles. The present study is one of the few attempts to model drivers’ responses to auditory cues in automated vehicles. It developed a mathematical model to quantify the effects of characteristics of auditory cues on drivers’ response to takeover requests in automated vehicles. The current study enhanced queuing network-model human processor (QN-MHP) by modeling the effects of different auditory warnings, including speech, spearcon, and earcon. Different levels of intuitiveness and urgency of each sound were used to estimate the psychological parameters, such as perceived trust and urgency. The model predictions of takeover time were validated via an experimental study using driving simulation with resultant R squares of 0.925 and root-mean-square-error of 73 ms. The developed mathematical model can contribute to modeling the effects of auditory cues and providing design guidelines for standard takeover request warnings for automated vehicles. 相似文献
802.
Shared autonomous vehicles (SAVs) are one of the important development directions of smart and green transportation. However, relevant researches are not sufficient at present. The factors influencing the intention to use SAVs and their parking choice behaviors need to be further analyzed. First, in order to better explain, predict, and improve travelers’ intention to use SAVs, the conceptual framework based on technology acceptance model was developed to establish the relationships between the travelers’ intention to use SAVs, social influence of SAVs, attitude toward behavior of SAVs, perceived risk of SAVs, perceived usefulness of SAVs and perceived ease of these use. Then structural equation model (SEM) was established to analyze the relationship between various variables. The results show that the perceived usefulness, behavior attitude, social influence, perceived ease of use, and perceived risk are the main factors that determine the intention to use SAVs. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on intention to use SAVs, with a standardized coefficient of 0.765, followed by behavior attitude (0.732), social influence (0.597), perceived ease of use (0.462) and perceived risk of SAVs (−0.452). In addition, through the study of observed indicator variables ATB2 and BI3, it is found that perceived usefulness, perceived ease of use, social influence, perceived risk, attitude toward behavior, and behavior intention all have an impact on parking behavior. In order to study the specific influencing factors of parking choice behavior, a multinomial logit (MNL) model was established to analyze the relationships between travelers’ parking choice behaviors and the influential factors, which include travelers’ individual characteristics, travel attributes, and parking modes’ attributes by extracting from a questionnaire. The results show that the travel time, travel fees, parking charge, cruising fees, parking time and traffic emission are the main factors that determine travelers’ choices of parking. This paper provides advice for operators of SAVs. 相似文献
803.
Memory and learning are essential functions in human beings as they allow us to acquire and store in the brain representations of thoughts, experiences, and behaviors, which are required for problem-solving in our daily life and mainly for survival. Episodic memory is a type of memory that provides the ability to re-experience events in one’s life, and it is associated with their conscious recollection. Since episodic memory can represent our experiences about the environment, similar to a mental journey, it is desired in systems that attempt to create human-like behavior. Currently, the main problem is that state of the art proposals do not consider neuroscientific evidence like memory dynamics for forgetting or bottom-up inputs, and most of them do not consider episodic memory as a different memory but as part of general declarative memory. We consider these omissions to limit the generation of human-like behavior. In this work, we propose a bio-inspired cognitive architecture of episodic memory. Neuroscientific evidence provides us with the brain structures associated with this type of memory, the connections, and the operations these areas perform. We hypothesize that virtual entities endowed with our episodic memory cognitive architecture will plan and make decisions in a more human-like fashion. To test the capabilities of the proposal, we endowed a virtual creature with a distributed and concurrent implementation of our architecture, and it was given two tasks. The first task validated the functions of the memory independently, and in the second task, the creature used episodic memory to solve a planning problem. From the results of these experiments, we validate our proposal and show that it is possible to create a system that behaves as the human brain does. 相似文献
804.
Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study. 相似文献
805.
To avoid collisions, pedestrians intending to cross a road need to accurately estimate the time-to-collision (TTC) of an approaching vehicle. For TTC estimation, auditory information can be considered particularly relevant when the approaching vehicle accelerates. The sound of vehicles with internal combustion engine (ICEVs) provides characteristic auditory information about the acceleration state (increasing rotational speed and engine load). However, for electric vehicles (EVs), the acoustic signature during acceleration is less salient. Although the auditory detection of EVs has been studied extensively, there is no research on potential effects of the altered acoustic signature of EVs on TTC estimation. To close this gap, we compared TTC estimates for ICEVs and for EVs with and without activated acoustic vehicle alerting system (AVAS). We implemented a novel interactive audiovisual virtual-reality system for studying the human perception of approaching vehicles. Using acoustic recordings of real vehicles as source signals, the dynamic spatial sound field corresponding to a vehicle approaching in an urban setting is generated based on physical modeling of the sound propagation between vehicle and pedestrian (listener) and is presented via sound field synthesis (higher-order Ambisonics). In addition to the auditory simulations, the scene was visually presented on a head-mounted display with head tracking. Participants estimated the TTC of vehicles that either approached at a constant speed or accelerated positively. In conditions with constant speed, TTC estimates for EVs with and without AVAS were similar to those for ICEVs. In contrast, for accelerating vehicles, there was a substantial effect of the vehicle type on the TTC estimates. For the EVs, the mean TTC estimates showed a significant overestimation. Thus, subjects on average perceived the time of arrival of the EV at their position as longer than it actually was. The extent of overestimation increased with acceleration and presented TTC. This pattern is similar to a first-order TTC estimation representing a failure to consider the acceleration, which is consistently reported in the literature for visual-only presentations of accelerating objects. In comparison, the overestimation of TTC was largely reduced for the accelerating ICEVs. The AVAS somewhat improved the TTC estimates for the accelerating EVs, but without reaching the same level of accuracy as for the ICEVs. In real traffic scenarios, overestimations of the TTC of approaching vehicles might lead to risky road-crossing decisions. Therefore, our finding that pedestrians are significantly less able to use the acoustic information emitted by accelerating EVs for their TTC judgments, compared to accelerating ICEVs, has important implications for road safety and for the design of AVAS technologies. 相似文献
806.
This study investigates acceleration behavior and crossing decision of the drivers under increasing time pressure driving conditions. A typical urban route was designed in a fixed-base driving simulator consisting of four signalized intersections with varying time to stop line (4 s and 6 s) and maneuver type (right-turn and go-through). 97 participants’ data were obtained under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. The acceleration behavior was examined at the onset of yellow signal in four ways: continuous deceleration, acceleration-deceleration, deceleration-acceleration, and continuous acceleration. A random forest model was used to build an acceleration behavior prediction model for identifying the significant explanatory variables based on variable importance ranking. Further, a Mixed Effects Multinomial Logit (MEML) model was developed using the explanatory variables obtained from a random forest model. Additionally, a generalized linear mixed model was incorporated for estimating the likelihood of crossing an intersection by considering all the explanatory variables. A MEML model result revealed that the odds of adopting acceleration-deceleration, deceleration-acceleration, and continuous acceleration instead of continuous deceleration increased by 63 %, 123 %, and 77 %, respectively under HTP driving conditions. Moreover, the likelihood of crossing a signalized intersection increased by 2.73 times and 4.26 times when the drivers were under LTP and HTP driving conditions, respectively as compared to NTP driving condition. Apart from this, time to stop line (reference: 6 s) and age showed negative association with crossing probability. Overall, the findings from this study revealed that drivers altered their acceleration behavior for executing risky driving decisions under increasing time pressure driving conditions. 相似文献
807.
To examine the public acceptance of connected vehicles (CVs), this study developed a novel connected vehicle acceptance model (CVAM) extending the technology acceptance model (TAM). The model was built based on a questionnaire survey of 2400 US adults. Perceived data privacy and security associated with the technology was found to shape the trust, attitude, and behavioral intention to use CVs, in addition to the predictors of original TAM: perceived usefulness and ease of use. Results revealed that trust mediates the effect of perceived data privacy and security on CV acceptance, though its directional relationship with perceived usefulness and perceived ease of use is unclear. Socio-demographic and other characteristics of respondents associated with CV acceptance and its predictors were explored. A number of theoretical and practical implications of the study findings are discussed. 相似文献
808.
809.
810.
《Quarterly journal of experimental psychology (2006)》2013,66(6):1148-1167
How do kindergarteners solve different single-digit addition problem formats? We administered problems that differed solely on the basis of two dimensions: response type (approximate or exact), and stimulus type (nonsymbolic, i.e., dots, or symbolic, i.e., Arabic numbers). We examined how performance differs across these dimensions, and which cognitive mechanism (mental model, transcoding, or phonological storage) underlies performance in each problem format with respect to working memory (WM) resources and mental number line representations. As expected, nonsymbolic problem formats were easier than symbolic ones. The visuospatial sketchpad was the primary predictor of nonsymbolic addition. Symbolic problem formats were harder because they either required the storage and manipulation of quantitative symbols phonologically or taxed more WM resources than their nonsymbolic counterparts. In symbolic addition, WM and mental number line results showed that when an approximate response was needed, children transcoded the information to the nonsymbolic code. When an exact response was needed, however, they phonologically stored numerical information in the symbolic code. Lastly, we found that more accurate symbolic mental number line representations were related to better performance in exact addition problem formats, not the approximate ones. This study extends our understanding of the cognitive processes underlying children's simple addition skills. 相似文献