Zobrazeno 1 - 10
of 285
pro vyhledávání: '"Flannagan, Carol"'
Autor:
Scanlon, John M., Teoh, Eric R., Kidd, David G., Kusano, Kristofer D., Bärgman, Jonas, Chi-Johnston, Geoffrey, Di Lillo, Luigi, Favaro, Francesca, Flannagan, Carol, Liers, Henrik, Lin, Bonnie, Lindman, Magdalena, McLaughlin, Shane, Perez, Miguel, Victor, Trent
The public, regulators, and domain experts alike seek to understand the effect of deployed SAE level 4 automated driving system (ADS) technologies on safety. The recent expansion of ADS technology deployments is paving the way for early stage safety
Externí odkaz:
http://arxiv.org/abs/2408.07758
Generating representative rear-end crash scenarios is crucial for safety assessments of Advanced Driver Assistance Systems (ADAS) and Automated Driving systems (ADS). However, existing methods for scenario generation face challenges such as limited a
Externí odkaz:
http://arxiv.org/abs/2406.15538
Real-time safety metrics are important for the automated driving system (ADS) to assess the risk of driving situations and to assist the decision-making. Although a number of real-time safety metrics have been proposed in the literature, systematic p
Externí odkaz:
http://arxiv.org/abs/2401.01501
The use of virtual safety assessment as the primary method for evaluating vehicle safety technologies has emphasized the importance of crash scenario generation. One of the most common crash types is the rear-end crash, which involves a lead vehicle
Externí odkaz:
http://arxiv.org/abs/2310.08453
Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using a
Externí odkaz:
http://arxiv.org/abs/2212.10024
With the ubiquitous availability of unstructured data, growing attention is paid as how to adjust for selection bias in such non-probability samples. The majority of the robust estimators proposed by prior literature are either fully or partially des
Externí odkaz:
http://arxiv.org/abs/2204.03215
The declining response rates in probability surveys along with the widespread availability of unstructured data has led to growing research into non-probability samples. Existing robust approaches are not well-developed for non-Gaussian outcomes and
Externí odkaz:
http://arxiv.org/abs/2203.14355
Big Data often presents as massive non-probability samples. Not only is the selection mechanism often unknown, but larger data volume amplifies the relative contribution of selection bias to total error. Existing bias adjustment approaches assume tha
Externí odkaz:
http://arxiv.org/abs/2101.07456
Publikováno v:
In Journal of Transport & Health January 2024 34
Naturalistic driving data (NDD) is an important source of information to understand crash causation and human factors and to further develop crash avoidance countermeasures. Videos recorded while driving are often included in such datasets. While the
Externí odkaz:
http://arxiv.org/abs/2011.14922