A human factors perspective on automated driving

ABSTRACT

Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees’ statements – which appear to be in line with a long history of HFs research – and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.     

Multi-mode reliability-based design of horizontal curves

Recently, reliability analysis has been advocated as an effective approach to account for uncertainty in the geometric design process and to evaluate the risk associated with a particular design. In this approach, a risk measure (e.g. probability of noncompliance) is calculated to represent the probability that a specific design would not meet standard requirements. The majority of previous applications of reliability analysis in geometric design focused on evaluating the probability of noncompliance for only one mode of noncompliance such as insufficient sight distance. However, in many design situations, more than one mode of noncompliance may be present (e.g. insufficient sight distance and vehicle skidding at horizontal curves). In these situations, utilizing a multi-mode reliability approach that considers more than one failure (noncompliance) mode is required. The main objective of this paper is to demonstrate the application of multi-mode (system) reliability analysis to the design of horizontal curves. The process is demonstrated by a case study of Sea-to-Sky Highway located between Vancouver and Whistler, in southern British Columbia, Canada. Two noncompliance modes were considered: insufficient sight distance and vehicle skidding. The results show the importance of accounting for several noncompliance modes in the reliability model. The system reliability concept could be used in future studies to calibrate the design of various design elements in order to achieve consistent safety levels based on all possible modes of noncompliance.     

Mechanical Analysis and Performance Optimization for the Lunar Rover’s Vane-Telescopic Walking Wheel

It is well-known that optimizing the wheel system of lunar rovers is essential. However, this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers. In this study, an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance. First, a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension, considering the complex terrain of the moon. Next, the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption. This was achieved via analysis of the wheel force during movement. Finally, the effectiveness of the proposed method was demonstrated by several simulation experiments. The newly designed wheel can protrude on demand and reduce energy consumption; it can be used as a reference for lunar rover development engineering in China.     

Relating traffic fatalities to GDP in Europe on the long term

Modeling road safety development can provide important insight into policies for the reduction of traffic fatalities. In order to achieve this goal, both the quantifiable impact of specific parameters, as well as the underlying trends that cannot always be measured or observed, need to be considered. One of the key relationships in road safety links fatalities with risk and exposure, where exposure reflects the amount of travel, which in turn translates to how much travelers are exposed to risk. In general two economic variables: GDP and unemployment rate are selected to analyse the statistical relationships with some indicators of road accident fatality risk.The objective of this research is to provide an overview of relevant literature on the topic and outline some recent developments in macro-panel data analysis that have resulted in ongoing research that has the potential to improve our ability to forecast traffic fatality trends, especially under turbulent financial situations. For this analysis, time series of the number of fatalities and GDP in 30 European countries for a period of 38 years (1975–2012) are used. This process relies on estimating long-term models (as captured by long term time-series models, which model each country separately). Based on these developments, utilizing state-of-the-art modelling and analysis techniques such as the Common Correlated Effects Mean Group estimator (Pesaran), the long-term elasticity mean value equals 0.63, and is significantly different from zero for 10 countries only. When we take away the countries, where the number of fatalities is stationary, the average elasticity takes a higher value of nearly 1. This shows the strong sensitivity of the estimate of the average elasticity over a panel of European countries and underlines the necessity to be aware of the underlying nature of the time series, to get a suitable regression model.     

How the choice of safety performance function affects the identification of important crash prediction variables

Across the nation, researchers and transportation engineers are developing safety performance functions (SPFs) to predict crash rates and develop crash modification factors to improve traffic safety at roadway segments and intersections. Generalized linear models (GLMs), such as Poisson or negative binomial regression, are most commonly used to develop SPFs with annual average daily traffic as the primary roadway characteristic to predict crashes. However, while more complex to interpret, data mining models such as boosted regression trees have improved upon GLMs crash prediction performance due to their ability to handle more data characteristics, accommodate non-linearities, and include interaction effects between the characteristics.An intersection data inventory of 36 safety relevant parameters for three- and four-legged non-signalized intersections along state routes in Alabama was used to study the importance of intersection characteristics on crash rate and the interaction effects between key characteristics. Four different SPFs were investigated and compared: Poisson regression, negative binomial regression, regularized generalized linear model, and boosted regression trees. The models did not agree on which intersection characteristics were most related to the crash rate. The boosted regression tree model significantly outperformed the other models and identified several intersection characteristics as having strong interaction effects.     

Leading to distraction: Driver distraction,lead car,and road environment

Driver distraction is strongly associated with crashes and near-misses, and despite the attention this topic has received in recent years, the effect of different types of distracting task on driving performance remains unclear. In the case of non-visual distractions, such as talking on the phone or other engaging verbal tasks that do not require a visual input, a common finding is reduced lateral variability in steering and gaze patterns where participants concentrate their gaze towards the centre of the road and their steering control is less variable. In the experiments presented here, we examined whether this finding is more pronounced in the presence of a lead car (which may provide a focus point for gaze) and whether the behaviour of the lead car has any influence on the driver's steering control. In addition, both visual and non-visual distraction tasks were used, and their effect on different road environments (straight and curved roadways) was assessed. Visual distraction was found to increase variability in both gaze patterns and steering control, non-visual distraction reduced gaze and steering variability in conditions without a lead car; in the conditions where a lead car was present there was no significant difference from baseline. The lateral behaviour of the lead car did not have an effect on steering performance, a finding which indicates that a lead car may not necessarily be used as an information point. Finally, the effects of driver distraction were different for straight and curved roadways, indicating a stronger influence of the road environment in steering than previously thought.     

Comparative assessment of stress transfer efficiency in tension and compression

Raman spectroscopy is used to get an insight into the microstructural aspects of the compressional behaviour of carbon fibre composites. This is done by a comparative assessment of the stress transfer efficiency in tension and compression in single-fibre discontinuous model geometries. It was found that the axial stress is transferred in the fibre through the generation of shear stresses at the interface. The mechanism of stress transfer is independent of the loading mode. Furthermore, the values of maximum interfacial shear stress are a function of the applied strain for both tension and compression loading. Significant differences were found, however, in the mode of failure of the two systems. In tension, interfacial failure initiates from the fibre ends at relatively high applied strains and the stress transfer efficiency is affected by the onset of matrix plasticity. On the other hand, in compression, deterioration of the stress transfer efficiency occurs prior to any noticeable interfacial failure at the fibre ends due to fibre collapse at low strains. Finally, it is worth noting that in compression, the fibre fragments remain in contact, and thus can still bear load.     

Development of acoustic denoising learning network for communication enhancement in construction sites

This paper presents a novel denoising approach based on deep learning and signal processing to improve communication efficiency. Construction activities take place when different trades come to the site for overlapped periods to perform their works, which may easily produce hazardous noise levels. The existence of noise affects workers' health issues, especially hearing and rhythm of the heart, and impacts communication efficiency between workers. The proposed approach employs signal processing technique to transform the noisy audio into image and utilize neural networks to extract noisy features and denoise the image. The denoised image is then converted to obtain the denoised audio. Experiments on reducing the side effect of several common noises in construction sites were conducted, compared with the performance of denoising using conventional wavelet transform. Standard objective measures, such as signal-to-noise ratio (SNR), and subjective measures, such as listening tests are used for evaluations. Our experimental results show that the proposed algorithm achieved significant improvements over the traditional method, as evidenced by the following quantitative results of median value: MSE of 0.002, RMSE of 0.049, SNR of 5.7 dB, PSNR of 25.8 dB, and SSR of 8.Results indicate that the proposed algorithm outperforms conventional denoising methods in terms of both objective and subjective evaluation metrics and have the potential to facilitate communication between site workers when facing different noise sources inevitably.     

Wettability and wettability modification methods of porous transport layer in polymer electrolyte membrane electrolysis cells (PEMEC): A review

As a key component of polymer electrolyte membrane electrolysis cells (PEMEC), the wettability of anode porous transport layer (APTL) plays an important role on the transport of gas and liquid water in the anode. This paper reviews the research progress on the materials, structural parameters and wettability of APTL, and discusses qualitatively the effect of wettability on the detachment characteristics of oxygen bubbles which are on the surface or in the internal pore channels of APTL by establishing force models of an oxygen bubble. For the surface wettability modification of titanium (Ti) fiber-based APTL, the feasibility of four surface chemical modification methods, namely silane coupling agent modification, dopamine (DA) modification, grafting modification based on Ultraviolet (UV) irradiation or supercritical fluid technology are analyzed, which makes up for the blank of review articles in this field. Finally, the above-mentioned chemical modification methods are possible research opportunities for the wettability transformation of APTL.