Safety assessment of automated vehicles: how to determine whether we have collected enough field data?

Abstract

Objective: The amount of collected field data from naturalistic driving studies is quickly increasing. The data are used for, among others, developing automated driving technologies (such as crash avoidance systems), studying driver interaction with such technologies, and gaining insights into the variety of scenarios in real-world traffic. Because data collection is time consuming and requires high investments and resources, questions like “Do we have enough data?,” “How much more information can we gain when obtaining more data?,” and “How far are we from obtaining completeness?” are highly relevant. In fact, deducing safety claims based on collected data—for example, through testing scenarios based on collected data—requires knowledge about the degree of completeness of the data used. We propose a method for quantifying the completeness of the so-called activities in a data set. This enables us to partly answer the aforementioned questions.

Method: In this article, the (traffic) data are interpreted as a sequence of different so-called scenarios that can be grouped into a finite set of scenario classes. The building blocks of scenarios are the activities. For every activity, there exists a parameterization that encodes all information in the data of each recorded activity. For each type of activity, we estimate a probability density function (pdf) of the associated parameters. Our proposed method quantifies the degree of completeness of a data set using the estimated pdfs.

Results: To illustrate the proposed method, 2 different case studies are presented. First, a case study with an artificial data set, of which the underlying pdfs are known, is carried out to illustrate that the proposed method correctly quantifies the completeness of the activities. Next, a case study with real-world data is performed to quantify the degree of completeness of the acquired data for which the true pdfs are unknown.

Conclusion: The presented case studies illustrate that the proposed method is able to quantify the degree of completeness of a small set of field data and can be used to deduce whether sufficient data have been collected for the purpose of the field study. Future work will focus on applying the proposed method to larger data sets. The proposed method will be used to evaluate the level of completeness of the data collection on Singaporean roads, aimed at defining relevant test cases for the autonomous vehicle road approval procedure that is being developed in Singapore.     

Exploring the determinants for adopting water conservation measures. What is the tendency of landowners when the resource is already at risk?

In Finland, the current water conservation policy sets equal incentives for water conservation, regardless of the environmental condition. Before any policy reform, it is vital to investigate the tendency of landowners to adopt water conservation measures. In this study, we were interested in examining adoption if the soil quality implies a high leaching risk and if the water quality is already poor. By combining survey data with GIS data, we analysed the effect of farm and farmer characteristics and attitudes towards adoption. Our probit models indicated that financial variables were the key determinants of adoption for active farmers, whereas for passive owners, adoption was also explained by attitudes. In contrast to our expectations, adoption in areas under risk was weakly supported by our estimates. Environmental awareness, providing it increases with risk, is not strong enough to motivate adoption. Targeted agri-environmental measures, even though costly, cannot be avoided, and spatially tailored measures can attract adopters in hotspot areas.     

Trade‐offs among Catch,Bycatch, and Landed Value in the American Samoa Longline Fishery

The interspecific preferences of fishes for different depths and habitats suggest fishers could avoid unwanted catches of some species while still effectively targeting other species. In pelagic longline fisheries, albacore (Thunnus alalunga) are often caught in relatively cooler, deeper water (>100 m) than many species of conservation concern (e.g., sea turtles, billfishes, and some sharks) that are caught in shallower water (<100 m). From 2007 to 2011, we examined the depth distributions of hooks for 1154 longline sets (3,406,946 hooks) and recorded captures by hook position on 2642 sets (7,829,498 hooks) in the American Samoa longline fishery. Twenty‐three percent of hooks had a settled depth <100 m. Individuals captured in the 3 shallowest hook positions accounted for 18.3% of all bycatch. We analyzed hypothetical impacts for 25 of the most abundant species caught in the fishery by eliminating the 3 shallowest hook positions under scenarios with and without redistribution of these hooks to deeper depths. Distributions varied by species: 45.5% (n = 10) of green sea turtle (Chelonia mydas), 59.5% (n = 626) of shortbill spearfish (Tetrapturus angustirostris), 37.3% (n = 435) of silky shark (Carcharhinus falciformis), and 42.6% (n = 150) of oceanic whitetip shark (C. longimanus) were caught on the 3 shallowest hooks. Eleven percent (n = 20,435) of all tuna and 8.5% (n = 10,374) of albacore were caught on the 3 shallowest hooks. Hook elimination reduced landed value by 1.6–9.2%, and redistribution of hooks increased average annual landed value relative to the status quo by 5–11.7%. Based on these scenarios, redistribution of hooks to deeper depths may provide an economically feasible modification to longline gear that could substantially reduce bycatch for a suite of vulnerable species. Our results suggest that this method may be applicable to deep‐set pelagic longline fisheries worldwide. Compensaciones entre Captura, Captura Accesoria y Valores Asentados en la Pesquera de Línea Larga de Samoa Americana     

大气污染联合治理分区视角下的中国PM2.5关联关系时空变异特征分析

联合治理分区下PM_(2.5)关联关系时空变异特征识别对中国大气污染防治意义重大.本文主要基于2000～2016年遥感反演的中国大陆334个地级市PM_(2.5)浓度数据,利用空间单元聚合策略与地理时空加权回归技术,系统分析了大气污染联合治理分区视角下的中国PM_(2.5)关联关系时空变异特征.结果表明:①以PM_(2.5)为首要污染物,综合考虑污染程度、地理位置、气象、地形和经济等因素可将中国大陆地区划分为10个大气污染联合治理区.②地理时空加权回归能够有效刻画PM_(2.5)与关联因素间的时空非平稳关系.同时,人口规模、第二产业生产总值、SO_2排放量、年平均气温、年降水量以及年平均相对湿度被识别出对PM_(2.5)浓度的变化影响存在显著时空差异.③人口规模对PM_(2.5)浓度的影响程度各年最大的地区均为京津冀蒙区域;川渝滇黔区域中第二产业生产总值对PM_(2.5)浓度影响程度变异度最大,在黑吉辽区域之外,SO_2排放量回归系数值均先随时间逐渐减小再增大最后又减小;各治理区中年平均温度对PM_(2.5)影响程度的时间变异程度较小;而年降水量与年平均相对湿度对PM_(2.5)影响程度在各区域中呈现不同的变异特征.     

黄河源植被时空变化及其对地形和气候的响应

基于2000~2019年MODIS归一化植被指数（NDVI）遥感数据,辅以同期气温、降水和地形数据,通过最大值合成、趋势分析及相关分析法,分析了黄河源区植被的时空变化特征及其对地形和气候变化的响应.结果表明：黄河源区植被NDVI整体处于中高水平,但空间差异显著,呈现由东南向西北递减的空间分布格局;近20a来,植被总体上呈现出变好的趋势.植被对高程和坡度响应明显,随着高程的增加,植被NDVI呈现先增加后减少的趋势,但在3500~4100m区间植被NDVI变化不显著;此外,植被NDVI随着坡度的增大呈现出先增大后减小的变化趋势,且在24~26°坡度带植被NDVI达到最大值.黄河源区植被受气温和降水的共同影响,与降水相比,气温对黄河源区植被变化的影响更为显著.     

基于DEM数据的HEC-HMS和Vflo降雨特征模拟对比研究——以北京密云区为例

选用密云地区30 m和90 m DEM数据并运用ArcGIS软件提取该地区DEM数据的水文地理信息,通过分析水文模型HEC-HMS、EPASWMM和Vflo后选择应用HEC-HMS和Vflo模型对该地区降雨进行分析.同时,统计分析密云地区过去30年(1989—2018年)的降雨数据,并结合研究区水文情况,基于密云地区普通降雨、暴雨、大暴雨和特大暴雨的降雨数据选用HEC-HMS和Vflo软件进行水文模拟.结果表明,HEC-HMS和Vflo软件可有效地模拟密云地区的降雨情况.HEC-HMS和Vflo软件输出的仿真结果很好,仿真模型的确定系数均大于0.82.分析HEC-HMS软件输出的普通降雨、暴雨、大暴雨、特大暴雨的降雨强度及其它参数,发现降雨强度越小模拟结果越接近实际数值,基于Vflo水文模拟软件也有相同结果.这同时也说明在对暴雨、大暴雨和特大暴雨进行水文模拟时需要开发或优化相应软件来满足实际需求,最终为生态系统建设提供一定的研究基础.     

完善CEMS数据采集处理传输全过程的技术规范

分布安装在各现场端的污染源排放自动监测设备可以实时采集到海量的监测数据,要将这些数据经过处理（统计/计算）自动远程传输到各级环境管理部门的监控中心,必须制定出涵盖数据采集处理传输全过程的技术规范.本文以固定污染源烟气排放连续监测系统（CEMS）为例,分析了现有相关技术规范的不足和缺失,以及由此产生的现状和问题;提出了以满足环境管理需求为目标,完善CEMS数据标准化的基本设想;详细论述了在数据时间类型、数据种类和格式、数据处理方法及数据传输格式等方面进行规范的具体建议;本文还提出了通过修改和增补现有技术规范,实施数据标准化基本设想的路线图.     

基于Bayes的电子元件高温贮存试验方法研究

将工厂试验数据作为先验信息,应用Bayes方法确定电子元件高温贮存试验的试样数量,其结果明显少于GJB 345A—2005中规定的试样数量。基于不同试验结果,通过构建寿命模型和确定先验分布,分别得到了电子元件的寿命分布和失效概率的Bayes估计。     

可靠性数据资源应用体系建设现状及发展趋势

阐述了可靠性数据的定义和来源,论述了可靠性数据资源的作用。重点介绍了国内外可靠性数据资源网络建设的状况和进展,以及电子元器件可靠性预计手册和非电产品可靠性预计手册的编制情况和进展。在此基础上,分析了可靠性数据资源未来应用的发展方向。