Automated vehicles (AVs) must be thoroughly evaluated before their release and deployment. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. In this paper, we propose an accelerated evaluation approach for AVs. The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. Frontal collision due to unsafe cut-ins is the target crash type of this paper. Human-controlled vehicles making unsafe lane changes are modeled as the primary disturbance to AVs based on data collected by the University of Michigan Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behaviors, which generate risky testing scenarios while preserving the statistical information so that the safety benefits of AVs in nonaccelerated cases can be accurately estimated. The cross-entropy method is used to recursively search for the optimal skewing parameters. The frequencies of the occurrences of conflicts, crashes, and injuries are estimated for a modeled AV, and the achieved accelerated rate is around 2000 to 20 000. In other words, in the accelerated simulations, driving for 1000 miles will expose the AV with challenging scenarios that will take about 2 to 20 million miles of real-world driving to encounter. This technique thus has the potential to greatly reduce the development and validation time for AVs.

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This document presents key findings from the light-vehicle field operational test conducted as part of the Integrated Vehicle-Based Safety Systems program. These findings are the result of analyses performed by the University of Michigan Transportation Research Institute to examine the effects of a prototype integrated crash warning system on driving behavior and driver acceptance. The light-vehicle platform included four integrated crash-warning subsystems (forward-crash, lateral-drift, lane-change/merge crash, and curve-speed warnings) installed on a fleet of 16 passenger cars and operated by 108 randomlysampled drivers for a period of six weeks each. Each car was instrumented to capture detailed data on the driving environment, driver behavior, warning system activity, and vehicle kinematics. Data on driver acceptance was collected through a post-drive survey, debriefings and focus groups. Key findings indicate that use of the integrated crash warning system resulted in improvements in lanekeeping, fewer lane departures, and increased turn-signal use. The research also indicated that drivers were slightly more likely to maintain shorter headways with the integrated system. No negative behavioral adaptation effects were observed as a result of drivers' involvement in secondary task behaviors. Drivers generally accepted the integrated crash warning system and 72 percent of all drivers said they would like to have an integrated warning system in their personal vehicles. Drivers also reported that they found the blind-spot detection component of the lane-change/merge crash warning system to be the most useful and satisfying aspect of the integrated system.

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BackgroundPreoperative differentiation between malignant and benign soft‐tissue masses is important for treatment decisions.Purpose/HypothesisTo construct/validate a radiomics‐based machine method for differentiation between malignant and benign soft‐tissue masses.Study TypeRetrospective.PopulationIn all, 206 cases.Field Strength/SequenceThe T1 sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352–550/2.75–19 msec. The T2 sequence was acquired with the following parameters: TR/TE, 700–6370/40–120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort.AssessmentTwelve machine‐learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively).Statistical Tests1) Demographic characteristics: a one‐way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ2 test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver‐operating characteristic curve (AUC) and accuracy (ACC) values.ResultsThe LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2.Data ConclusionA machine‐learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft‐tissue masses.Evidence Level3Technical EfficacyStage 2 J. Magn. Reson. Imaging 2020;52:873–882.

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It is important to rigorously and comprehensively evaluate the safety of Automated Vehicles (AVs) before their production and deployment. A popular AV evaluation approach is Naturalistic-Field Operational Test (N-FOT) which tests prototype vehicles directly on public roads. Due to the low exposure to safety-critical scenarios, N-FOTs is time-consuming and expensive to conduct. Computer simulations can be used as an alternative to N-FOTs, especially in terms of generating motions of the surrounding traffic. In this paper, we propose an accelerated evaluation approach for AVs. Human-controlled vehicles (HVs) were modeled as disturbance to AVs based on data extracted from the Safety Pilot Model Deployment Program. The cut-in scenarios are generated based on skewed statistics of collected human driver behavior, which amplifies riskier testing scenarios while reserves its statistical information so that the safety benefits of AV in non-accelerated cases can be accurately estimated. An AV model based on a production vehicle was tested. Results show that the proposed method can accelerate the evaluation process by at least 100 times.