Advanced Alarm Method Based on Driver’s State in Autonomous Vehicles

The Effects of Masking on the Detection of Alarms in Close Temporal Proximity

To measure and compare the response times to audibly or visually presented alarms in the operating theatre. The time taken by anaesthetists to cancel randomly generated visual and audible false alarms was measured during maintenance of routine anaesthesia. Alarms were generated and times recorded by a laptop computer on the anaesthetic machine. The visual signal was a 15 mm diameter red light positioned next to the physiological monitor mounted on top of the machine. The audible alarm was a Sonalert buzzer of the type incorporated into many medical devices. Nineteen anaesthetists provided a total of seventy-two hours of data (887 alarm events). The response times to visual alarms was significantly longer than to audible alarms (P = 0.001 Mann Whitney U test). [Table: see text] The ability of anaesthetists to appreciate changes in patient physiology may be limited by delays in noticing information presented by monitors. The rapid response to the vast majority of alarms indicates a high level of vigilance among the anaesthetists studied. However, this study suggests that it is safer to rely on audible rather than visual alarms when time-critical information such as oxygenation, heart beat and ventilator disconnection is concerned. Visual alarms would appear to be more appropriate for conveying less urgent information.

Autonomous vehicles (AVs), which can be fully controlled by remote/online operators, could be an extension of ride-sourcing services provided by transportation network companies (TNCs). Meanwhile, substitutive and complementary relationships between ride-sourcing and public transit could also help TNCs increase their profit under certain strategies. Unlike previous studies that generally ignored either AVs or interactions between public transit and ride-sourcing, we introduce AV-involved operational strategies into a multi-modal system. In this paper, we focus on transportation hubs in an urban area and let the TNC assign AVs to these hubs for serving only within that particular hub at a differentiated fare; meanwhile, passengers could choose among AVs, human-driven vehicles (HVs), combined modes with public transit and AVs or HVs, or other travel modes to fulfill their travel needs. We develop a mathematical model to investigate the impacts of such an operational strategy, and formulate an optimisation problem to maximise the TNC's profit and minimise total waiting time simultaneously by adjusting AV fare and fleet size. We further propose a comprehensive modeling framework with the analytical model, optimisation problem, calibration method, and heuristic algorithm, making it a general approach for different real-world scenarios. Following the framework, we conduct a case study based on real-world datasets of public transit and ride-sourcing services in Hangzhou, China. Different market schemes are analyzed and compared with the currently existing situation. The results demonstrate that by adopting this auxiliary-AV-oriented operational strategy, the TNC's profit and public transit ridership can both be increased, and passengers could enjoy a shorter waiting time for HV ride-sourcing trips. Moreover, the TNC is more inclined to allocate more AVs to hubs with large commute needs and uncongested traffic, leading to a high profit for the TNC and a short waiting time for passengers. Societal preferences towards AV trips are also analyzed. The results provide in-depth references for real-world AV-related ride-sourcing operational problems.

Over the next decade, autonomous vehicles (AVs) will become increasingly prevalent throughout the United States (U.S.), transforming the nation’s transportation landscape. However, there is no guarantee that the potential benefits offered by AVs will be fully realized or that AV providers will serve the public interest in addition to their bottom line. The Federal, State, and Local Governments in the U.S., for the most part, have not effectively prepared for the inevitable arrival of AVs. Therefore, when our team assessed the current AV regulatory environment, we identified under-regulation at the federal level, a mix of disconnected regulations at the state and municipal levels, and a set of diverse stakeholders across all levels of government who are uncertain of the path forward. To produce the greatest impact, we chose to focus our analysis on the municipal-level governance for several reasons. First, municipalities have been the driving force for innovation and experimentation in AV testing and pilots. Second, the real thought leadership on AV regulation has occurred mainly at the municipal level, as federal and state regulations have provided the base upon which municipal regulations are layered. Third, municipal governments have faced the highest amount of uncertainty and risk surrounding AV implementation. To arrive at a prioritized set of recommendations local governments and AV providers, we used a proprietary “4P Framework,” which assigns equal weighting and consideration to (1) People, (2) Profit, (3) Planet, and (4) Possibility. After a thorough evaluation of these criteria, we focused on six key recommendations: - 1. Implementing A Flexible Curb-use Model with Dynamic Pricing; - 2. Creating an AV Piloting Checklist; - 3. Appling Geofencing to Urban Core; - 4. Prohibiting Digita Redlining; - 5. Defining Clear Rules of Data Ownership, Use, and Retention; and - 6. Taxing Low-Occupancy/“Zombie” Cars. Although we arrive at a set of effective practices recommendations for municipalities to consider, the value of these series of articles is in guiding the development of AV regulation holistically rather than prescribing specific recommendations. As such, municipalities should use this report as a starting point and determine appropriate policies based on the context of their specific situation, adapting the effective practices to best align to their policy goals.

It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction; these problems seriously affect the safety and comfort of mixed traffic systems. Therefore, the ability of autonomous vehicles to predict the driving direction in real time according to the surrounding traffic environment must be improved and researchers must work to create a more mature ITS. In this paper, we propose a deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled ITS. In this scheme, a driving trajectory dataset and a natural-driving dataset are employed as the network inputs to long-term memory networks in the 5G-enabled ITS: the probability matrix of each intention is calculated by the softmax function. Then, the final intention probability is obtained by fusing the mean rule in the decision layer. Experimental results show that the proposed scheme achieves intention recognition rates of 91.58% and 90.88% for left and right lane changes, respectively, effectively improving both accuracy and real-time intention recognition and improving the lane change problem in a mixed traffic environment.

Public perceptions have been playing an important role in the development of autonomous vehicle (AV) technology. Besides AV and non-AV users, the perceptions of vulnerable roadway users are critical, as AVs will become a part of multimodal transportation system. Pedestrians and bicyclists are among the vulnerable groups of roadway users, as they are relatively unprotected compared to the occupants of AVs or non-AVs. Although AV’s capability to monitor other vehicles has been documented in many studies, there are concerns about AV’s capability in monitoring pedestrians and bicyclists. The overarching goal of this dissertation is to investigate the perceptions of pedestrians and bicyclists on AVs to understand and incorporate their perceptions in AV technology development. The specific research objectives are to- (i) categorize the positive and negative perceptions and regulation expectations of pedestrians and bicyclists, (ii) identify factors influencing AV road sharing related safety perceptions among pedestrians and bicyclists, (iii) understand pedestrians’ and bicyclists’ expectations on AV regulations and identify relevant factors influencing their attitudes towards AV regulations, and (iv) investigate the effectiveness of widely used close-ended rating-based quantitative survey question to assess AV perceptions among pedestrians and bicyclists. Two surveys conducted by Bike Pittsburgh (BikePGH) were used to accomplish the research objectives. In addition to quantitative responses, BikePGH surveys collected open-ended responses to understand the reasons for pedestrians’ and bicyclists’ quantitative responses. A combined inductive and deductive qualitative data analysis approach was applied to classify pedestrians’ and bicyclists' positive and negative perceptions and regulation expectations. Pedestrians and bicyclists expressed comparatively fewer negative opinions towards AVs than positive opinions. Negative opinions included a lack of safety and comfort around AVs and trust in the AV technology. Respondents also concerned about AV technology issues (e.g., slow and defensive driving, disruptive maneuvers). Pedestrians’ and bicyclists’ opinions were significantly influenced by their views on AV safety, familiarity with the AV technology, exposure to AV-related news, and household automobile ownership. Regulating AV movement on public roadways, developing safety assessment guidelines, and controlling oversights of AV technology developers' improper practices were the survey participants' noteworthy suggestions. Non-parametric statistical tests were conducted to compare the safety perceptions of pedestrians and bicyclists based on their characteristics, experiences, and attitudes. An ordered probit model was estimated to quantify the influence of different factors on safety perceptions of pedestrians and bicyclists regarding road sharing with AVs. In addition, safety perceptions and the effect of various factors on AV safety perceptions

Reaction time (RT) methods have been a mainstay of research in cognitive psychology for over a century. RT methods have been applied in domains as diverse as visual perception (e.g., Ando et al., 2002), personality traits (e.g., Robinson and Tamir, 2005), and social psychology (e.g., Wang et al., 2017). In music cognition, RT methods have been used as an indirect measure of several phenomena such as harmonic expectation (Bharucha and Stoeckig, 1986), melodic expectation (Aarden, 2003) cross modal priming (Goerlich et al., 2012), absolute pitch (Miyazaki, 1989; Bermudez and Zatorre, 2009), and emotional responses (Bishop et al., 2009). Traditionally, reaction time data has been collected in a lab. However, recent years have seen the development of software capable of collecting accurate response time data online, for instance PsyToolkit (Stoet, 2010, 2017), PsychoPy (Peirce et al., 2019), Gorilla (Anwyl-Irvine et al., 2019), and Qualtrics' QRTEngine (Barnhoorn et al., 2015) amongst others. In the early days of web-based reaction time studies, there was considerable skepticism about the viability of RT data collected online. Despite the prevalence of software specifically designed to collect reaction time data online, and the increasing incidence of Web-based data collection, there remains a degree of caution around online reaction time studies. However, recent research (Barnhoorn et al., 2015; de Leeuw and Motz, 2016; Hilbig, 2016) suggests that online reaction time data is perhaps more trustworthy than was previously thought, but these studies have not yet involved music as stimuli. Alongside the developments in software, recruitment of participants in online studies has been made easier by the prevalence of social media and crowdsourcing platforms such as Amazon's MTurk service and Prolific. Not surprisingly, the use of crowdsourced samples by researchers is growing rapidly (Stewart et al., 2017). However, to the authors' knowledge (with the exception of de Leeuw and Motz, 2016) the comparisons of laboratory and online RT data have focused on descriptive measures of the RT distributions, and relatively little attention has been paid to the agreement between the RT distributions as a whole. Moreover, none of these studies considers phenomena associated with music cognition. Given the widespread use of RT methods in music cognition and the growth of crowdsourcing as a recruitment tool, the authors consider there to be a need to test the viability of online RT collection specifically in the case of music cognition. The present data report offers the results of a response time task completed in three different contexts—in a standard lab setting (“Lab”), online recruited via “traditional” online techniques (“Web”) and crowdsourced vis Prolific.ac (“CS”). Below, we present summary data for the three data sets before testing the comparability of the three data sets on an item-by-item basis.

Reaction time (RT) data obtained from simple tonal detection tasks have been used to estimate frequency-specific equal-loudness contours in non-human animals. In order to guide the design of auditory weighting functions for marine mammals, equal-latency contours were generated using RT data from a simple tonal detection including two bottlenose dolphins (under water) and three California sea lions (in air). Median RT increased exponentially with decreased SPL in all cases. Equal-latency contours for near-threshold RTs were similar to audiograms in both species. Data for the sea lions showed some compression of equal-latency contours with increases in SPL; however, large inter-subject differences in the data for dolphins made results for that species more difficult to interpret. The equal-latency contours for all subjects progressively diverged from predicted equal-loudness contours at higher SPLs, likely a result of very small changes in RT with relatively large increases in SPL. As a result, the contours of most interest for designing weighting functions for high-level noise exposures were also the least reliable. The general similarity of most of the contours to species-typical audiograms suggests that more easily obtained auditory thresholds may provide useful approximations for weighting. [Funded by U.S. Navy Living Marine Resources Program.]

Perceptions of vulnerable roadway users on autonomous vehicle regulations

This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.

In the last decades autonomous vehicles have been at the centre of the research in both the academic and the industrial fields, but not without difficulties. In particular, the problem of path planning and tracking at the limit of the handling capabilities of a vehicle poses many challenges from a control perspective, and it is yet to be understood whether the integration with stability controllers can improve the cornering performance of autonomous vehicles as much as it does for human drivers. This thesis aims to provide insights on these topics. The first part of the work is dedicated to the planning and tracking layers of an autonomous vehicle driving on racetracks. The analysis covers the offline optimisation of the trajectory and the description of a re-planning algorithm for the avoidance of obstacles. A comparison among several path tracking controllers is then provided, to understand whether the gain in performance obtained from advanced controllers justifies the design complexity. In the second part, the thesis highlights the benefits of yaw rate control on the behaviour of over-actuated vehicles. An algorithm for yaw rate control is introduced and implemented in a torque vectoring controller, and the proof of asymptotic stability of the system is provided. Several application examples are presented, with simulation and experimental results that demonstrate the potential and versatility of yaw rate control. Finally, the integration of torque vectoring and path tracking control in an autonomous racing vehicle is presented and assessed with a simulation study along obstacle avoidance tests. The results of the thesis show that: i) including road preview information in path tracking controllers improves the control action, resulting in better vehicle behaviour, and ii) torque vectoring control always improves the vehicle performance, and it also enhances the system robustness to variations in the tyre-road friction coefficient.

Effects of caffeine withdrawal on motor performance and heart rate changes

Most analyses of reaction time (RT) data are conducted by using the statistical techniques with which psychologists are most familiar, such as analysis of variance on the sample mean. Unfortunately, these methods are usually inappropriate for RT data, because they have little power to detect genuine differences in RT between conditions. In addition, some statistical approaches can, under certain circumstances, result in findings that are artifacts of the analysis method itself. A corpus of research has shown more effective analytical methods, such as analyzing the whole RT distribution, although this research has had limited influence. The present article will summarize these ad’ances in methods for analyzing RT data.

Do remembering and knowing differ qualitatively (reflecting distinct underlying processes) or quantitatively (reflecting different levels of strength)? Broadly speaking, models of remember-know judgments based on these alternatives have been tested by examining the proportion of remember and know responses that are made across conditions or levels of confidence. Here, we consider reaction time (RT) data. We replicate Dewhurst and Conway's (1994) observation that old judgments followed by remember responses are faster, on average, than those followed by know decisions, but show that this effect is largely due to differing distributions of remember and know responses across confidence levels. In addition, fits of ex-Gaussian distributions of hit RTs followed by either remember or know judgments yield similar parameter values when confidence level is controlled. Thus, these RT data do not provide strong support for the idea that remembering and knowing depend on different processes.

Reaction times (RTs), saccadic eye movements, and fixation durations were measured while older and younger observers searched visual displays for targets defined by a single feature, luminance contrast or orientation, as well as the conjunction of these two features. Target eccentricity was varied between approximately 4 and 14 deg. Age deficits generally increased in the more difficult conditions. the RT data indicated that age deficits were greatest for conjunction search and on target absent trials. the saccade data showed that on target present trials, age deficits were larger for more eccentric targets, especially in conjunction search. Fixation durations were related by a power function to the number of saccades made prior to a correct response. Whereas the exponent of these power functions was constnat across search condition and age group, the coefficient was larger for older adults. Thus at a fixed number of saccades, when old and young were presumably searching groups of equal size, the elderly took longer to process the information within that group. the RT, fixation duration, and saccade data of older observers were fit by a linear function based on the performance of young adults, and all dependent measures yielded essentially the same slowing function. This pattern was also found when the RT and saccade data from Scialfa, Thomas, and Joffe (1994) were reexamined. Results are discussed with reference to the generalized slowing hypothesis and models of visual search.