Functional decomposition—A contribution to overcome the parameter space explosion during validation of highly automated driving

Abstract

Objective: Particular testing by functional decomposition of the automated driving function can potentially contribute to reducing the effort of validating highly automated driving functions. In this study, the required size of test suites for scenario-based testing and the potential to reduce it by functional decomposition are quantified for the first time.

Methods: The required size of test suites for scenario-based approval of a so-called Autobahn-Chauffeur (SAE Level 3) is analyzed for an exemplary set of scenarios. Based on studies of data from failure analyses in other domains, the possible range for the required test coverage is narrowed down and suitable discretization steps, as well as ranges for the influence parameters, are assumed. Based on those assumptions, the size of the test suites for testing the complete system is quantified. The effects that lead to a reduction in the parameter space for particular testing of the decomposed driving function are analyzed and the potential to reduce the validation effort is estimated by comparing the resulting test suite sizes for both methods.

Results: The combination of all effects leads to a reduction in the test suites’ size by a factor between 20 and 130, depending on the required test coverage. This means that the size of the required test suite can be reduced by 95–99% by particular testing compared to scenario-based testing of the complete system.

Conclusions: The reduction potential is a valuable contribution to overcome the parameter space explosion during the validation of highly automated driving. However, this study is based on assumptions and only a small set of exemplary scenarios. Thus, the findings have to be validated in further studies.     

Effect of alcohol use on accelerating and braking behaviors of drivers

Objective: The present study examines the accelerating and braking behaviors of drivers at different blood alcohol concentrations (BACs) in heterogeneous driving conditions using driving simulator experiments.

Methods: Eighty-two licensed drivers performed simulated driving in a rural road environment designed in the driving simulator at 4 BAC levels: 0.00, 0.03, 0.05, and 0.08%. Driving performance was analyzed using vehicle control variables such as mean acceleration and mean brake pedal force. Generalized linear mixed models were developed to quantify the effect of different alcohol levels and explanatory variables such as driver’s age, gender, and other factors on the driving performance indicators.

Results: Alcohol use was reported as a significant factor affecting the accelerating and braking performance of drivers. The acceleration model results indicated that drivers’ mean acceleration increased by 0.013, 0.026, and 0.027 m/s² for BAC levels of 0.03, 0.05, and 0.08%, respectively. Results of the brake pedal force model showed that drivers’ mean brake pedal force increased by 1.09, 1.32, and 1.44 N for BAC levels of 0.03, 0.05, and 0.08%, respectively. Age was a significant factor in both the models where a 1-year increase in driver age resulted in a 0.2% reduction in mean acceleration and a 19% reduction in mean brake pedal force. Driving experience could compensate for the negative effects of alcohol to some extent while driving.

Conclusions: The findings of the present study revealed that drivers tend to be more aggressive and impulsive under the influence of alcohol, which deteriorates their driving performance. Impairment in accelerating and braking behaviors of drivers under the influence of alcohol leads to increased crash probabilities. The conclusions may provide reference in making countermeasures against drinking and driving and contribute to traffic safety.     

Driving anger expression in Germany—Validation of the Driving Anger Expression Inventory for German drivers

Objective: The main objective of this article is to examine whether the Driving Anger Expression Inventory (DAX) applies to German drivers because this scale has previously been given to drivers in many different countries.

Methods: We applied German versions of the DAX, the Driving Anger Scale (DAS), and the State-Trait Anger Expression Inventory (STAXI) to a sample of 501 German drivers. We computed confirmatory factor analysis and principal axis factoring (PAF) analysis to examine the structure of driving anger expression in Germany. Finally, we related the drivers’ anger exp ression scores to their driving anger experiences and their general anger propensities to assess the validity of the DAX for German drivers.

Results: Results indicated that the DAX’s original factor structure does not apply to German drivers because the confirmatory factor analysis did not show a good model fit. An item analysis revealed that many DAX items had no meaningful variability. They were excluded from further analysis. The subsequent PAF analysis indicated that German drivers do not use personal physical aggression to express their driving anger. Instead, they reported unique preventive anger expression management behavior. In addition, their driving anger expressions were significantly related to their driving anger experiences and their general anger propensities indicated the validity of the refined DAX for German drivers.

Conclusions: We conclude that German drivers do not use strong behaviors to express their driving anger. Many statements of Deffenbacher et al.’s (Behav Res Ther. 40:717–737, 2002) original American questionnaire were not applicable for our sample of German drivers. These findings are in line with several other studies showing discrepancies in driving anger expression in various countries. Future investigations should examine the reasons for discrepancies in driving anger expression.     

Test procedure for evaluating the human–machine interface of vehicles with automated driving systems

Abstract

Objective: The human–machine interface (HMI) is a crucial part of every automated driving system (ADS). In the near future, it is likely that—depending on the operational design domain (ODD)—different levels of automation will be available within the same vehicle. The capabilities of a given automation level as well as the operator’s responsibilities must be communicated in an appropriate way. To date, however, there are no agreed-upon evaluation methods that can be used by human factors practitioners as well as researchers to test this.

Methods: We developed an iterative test procedure that can be applied during the product development cycle of ADS. The test procedure is specifically designed to evaluate whether minimum requirements as proposed in NHTSA’s automated vehicle policy are met.

Results: The proposed evaluation protocol includes (a) a method to identify relevant use cases for testing on the basis of all theoretically possible steady states and mode transitions of a given ADS; (b) an expert-based heuristic assessment to evaluate whether the HMI complies with applicable norms, standards, and best practices; and (c) an empirical evaluation of ADS HMIs using a standardized design for user studies and performance metrics.

Conclusions: Each can be used as a stand-alone method or in combination to generate objective, reliable, and valid evaluations of HMIs, focusing on whether they meet minimum requirements. However, we also emphasize that other evaluation aspects such as controllability, misuse, and acceptance are not within the scope of the evaluation protocol.     

Analysis on Temporal and Spatial Changes and Driving Forces of Poverty-Stricken Areas in Hebei Province

Abstract

This paper takes poverty-stricken county as the basic unit, and selects net income per peasant to study the poverty status in Hebei Province during 1986–2000. Temporal and spatial changes of poverty-stricken areas are analyzed. The result shows that poverty-stricken areas in Hebei Province distributed concentratedly and the areas decreased during 1986–2000, the net income per peasant was on the rise with an increasing speed in off-poverty counties being slightly higher than that in poverty-stricken counties, but the growth rate was extremely unstable, rising slowly in off-poverty counties while dropping in poverty-stricken counties. The main driving forces that influenced temporal and spatial changes were economic development of the whole province, ecological environment quality, infrastructure conditions and radiation of the key city. On this basis, some anti-poverty countermeasures suitable to local conditions are proposed.     

庞泉沟自然保护区土壤呼吸空间分异性影响因素探测

土壤呼吸是维持全球碳平衡的重要过程.以庞泉沟自然保护区为研究区域,基于实地测量的土壤呼吸（R_s）数据,结合海拔（ELE）、土壤温度（T）、土壤水分（SWC）、归一化植被指数（NDVI）、坡度（slope）、土壤全碳（C）、总氮（N）和土壤容重（BD）,运用地理探测器模型分析了R_s空间分异的主要驱动力及其交互作用.结果表明：①研究区R_s及其影响因子的空间变异均为中等变异.R_s与NDVI、T和土壤N呈极显著正相关（P<0.01）,与ELE、slope和SWC呈极显著负相关（P<0.01）,R_s与BD显著负相关（P<0.05）,与C相关性不显著（P>0.05）.②NDVI和T构成的多元线性模型可以解释R_s空间分异的64.3%.③ELE、T和NDVI为研究区R_s空间分异性的主导驱动力,可以解释64%、59%和48%的空间变异.④双因子的交互作用均能增强对R_s空间分异性的解释力,最大交互因子分别为ELE∩BD （q=0.73）和T ∩ slope （q=0.74）.因此,在R_s的估算过程中,结合地形及环境条件,应考虑多种因子之间的交互作用.     

驾驶员接管自动驾驶车辆的眼动特性和行为分析

为探究接管自动驾驶车辆期间驾驶员的视觉特性,分析眼动与接管反应操控行为的关系,开展驾驶模拟试验收集驾驶行为及眼动数据。运用统计学方法,分析驾驶员感知不同接管场景的视觉特性,探究接管请求(TOR)前后眼动指标的变化规律;并基于视觉分配和瞳孔变化特性分析驾驶行为,揭示眼动特性与接管反应及驾驶操纵策略的内在联系。结果表明:TOR前,相较于静态场景,驾驶员感知动态场景诱发元素扫视更频繁且平均注视时间更短;此时驾驶员的视觉分配特性与其接管反应行为存在显著相关性。TOR后,驾驶员的注视时间增加,眨眼频率降低,瞳孔直径扩张,眼跳幅度增大;不同场景下驾驶员的瞳孔差异表明其应对动态场景时具备更好的警戒水平和更平稳的操纵策略。     

基于Logistic回归模型的大兴安岭地区林火发生概率预测研究*

为实现对大兴安岭地区林火发生概率的基本预测,根据该地区历史火灾资料、地形因子、气象因子和人为因子,基于Logistic回归模型建立该地区林火发生概率预测模型。研究结果表明:坡度、海拔、平均气压、平均气温、平均相对湿度、最小相对湿度是该地区林火发生的主要驱动因子;坡度、海拔、平均气压、平均气温与林火发生概率呈正相关,平均相对湿度和最小相对湿度与林火发生概率呈负相关;ROC曲线下面积值（AUC）为0.91,最佳临界值为0.425;建立的大兴安岭地区林火发生概率预测模型,建模样本总体准确率为82.4%,验证样本总体准确率为80.5%;该地区夏季林火发生概率明显高于春、秋季;林火发生概率较高的Ⅳ级和Ⅴ级火险区主要集中在夏季该地区东南部、西部以及春、秋季该地区东南部。研究结果可为该地区林火预测提供1种参考方法。     

重点生态功能区生态资产保护成效及驱动力研究

重点生态功能区是保障国家生态安全、扩大绿色生态空间的重要区域,在实现全国生态资产保值增值方面发挥着重要作用。然而,现阶段围绕重点生态功能区生态资产保护成效及驱动力开展的研究较少。为明确重点生态功能区生态资产保护的驱动力以及存在的问题,本文利用遥感影像并结合图像解译、GIS空间分析和数理统计等方法对重点生态功能区内生态资产展开分析,并将其与非重点生态功能区进行比较。研究结果显示:重点生态功能区内森林、灌丛、草地和湿地生态资产总面积为349.30万km~2,优级和良级生态资产占生态资产总面积的42.42%,生态资产综合指数为21.19。15年间,重点功能区内生态资产总面积增加0.05%,优级和良级生态资产面积占比分别增加0.59%和0.85%,生态资产综合指数增加2.94。重点生态功能区生态资产面积和质量增幅均小于非重点生态功能区,然而重点生态功能区生态资产指数增量高于非重点生态功能区。生态恢复是驱动重点生态功能区生态资产面积变化的主因,对原有生态资产的保护是促进生态资产指数增加的最主要驱动力(贡献率达81%),城镇化和农业开发对生态资产的不利影响不容忽视(贡献率达-6.81%)。研究表明,对重点生态功能区生态资产保护成效显著,同时还有较大提升空间。本研究也为优化重点生态功能区的城镇化和农业开发的管理提供了证据和参考。     

情绪对新手驾驶员视觉特性的影响研究

为构建基于驾驶员生理特性情绪识别系统,采用眼动仪等设备对20名新手驾驶员开展不同情绪状态下的模拟驾驶试验,采集高兴、悲伤、愤怒3种情绪状态和无情绪下新手驾驶员的视觉数据,利用Matlab、SPSS软件统计分析新手驾驶员瞳孔变化、眼球扫视、注视点视觉数据.结果 表明:悲伤、愤怒情绪对新手驾驶员瞳孔尺寸分布频率及其变化差异...