This work provides a comprehensive analysis on naturalistic driving behavior for highways based on the highD data set. Two thematic fields are considered. First, some macroscopic and microscopic traffic statistics are provided. These include the traffic flow rate and the traffic density, as well as velocity, acceleration and distance distributions. Additionally, the dependencies to each other are examined and compared to related work. The second part investigates the distributions of criticality measures. The Time-To-Collision, Time-Headway and a third measure, which couples both, are analyzed. These measures are also combined with other indicators. Scenarios, in which these measures reach a critical level, are separately discussed. The results are compared to related work as well. The two main contributions of this work can be stated as follows. First, the analysis on the criticality measures can be used to find suitable thresholds for rare traffic scenarios. Second, the statistics provided in this work can also be utilized for traffic modeling, for example in simulation environments.
These two indicators fail for crossing scenarios. In such cases the indicators have to be adapted or replaced as discussed for example in @cite_4 .
@cite_4
2510089447
Abstract This paper addresses the problem of how city indicators and their supporting data are to be published on the Semantic Web so that automated analysis can be performed. With the publishing of ISO 37120, cities have a standard set of indicators that can be used to compare their performance. The problem is that no standards exist for publishing indicators on the Semantic Web. In this paper we introduce the Global City Indicator Ontology (GCIO). The GCIO addresses five issues: 1) how is meta-data associated with a single indicator value represented? 2) how are indicator definitions represented? 3) how is the data used to derive an indicator value represented? 4) how is indicator theme specific knowledge represented, and 5) how is city specific knowledge represented? The GCIO has been implemented and validated using the City of Toronto ISO 37120 indicators reported for 2013. Research continues in developing ontologies specifically for each of the indicator themes such as: Education, Shelter, Health, Transportation and Innovation. This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popular algorithms, namely NSGA-II and SPEA2, with respect to different performance measures. 1. Introduction, 355. — 2. Hiring as investment under uncertainty, 356. — 3. Applicant signaling, 358. — 4. Informational feedback and the definition of equilibrium, 359. — 5. Properties of informational equilibria: an example, 361. — 6. The informational impact of indices, 368. — Conclusions, 374. A new data structure for efficient similarity search in very large datasets of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and pre-processing time, inverted multi-indices achieve a much denser subdivision of the search space compared to inverted indices, while retaining their memory efficiency. Our experiments with large datasets of SIFT and GIST vectors demonstrate that because of the denser subdivision, inverted multi-indices are able to return much shorter candidate lists with higher recall. Augmented with a suitable reranking procedure, multi-indices were able to significantly improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead.