Vehicle crash simulations for safety: Introduction of connected and automated vehicles on the roadways.

Traffic accidents are one main cause of human fatalities in modern society. With the fast development of connected and autonomous vehicles (CAVs), there comes both challenges and opportunities in improving traffic safety on the roads. While on-road tests are limited due to their high cost and hardware requirements, simulation has been widely used to study traffic safety. To make the simulation as realistic as possible, real-world crash data such as crash reports could be leveraged in the creation of the simulation. In addition, to enable such simulations to capture the complexity of traffic, especially when both CAVs and human-driven vehicles co-exist on the road, careful consideration needs to be given to the depiction of human behaviors and control algorithms of CAVs and their interactions. In this paper, the authors reviewed literature that is closely related to crash analysis based on crash reports and to simulation of mixed traffic when CAVs and human-driven vehicles co-exist, for studying traffic safety. Three main aspects are examined based on our literature review: data source, simulation methods, and human factors. It was found that there is an abundance of research in the respective areas, namely, crash report analysis, crash simulation studies (including vehicle simulation, traffic simulation, and driving simulation), and human factors. However, there is a lack of integration between them. Future research is recommended to integrate and leverage different state-of-the-art transportation-related technologies to contribute to road safety by developing an all-in-one-step crash analysis system.

Investigation of preclinical pharmacokinetics of N-demethylsinomenine, a potential novel analgesic candidate, using an UPLC-MS/MS quantification method.

N- Demethylsinomenine (NDSM), the in vivo demethylated metabolite of sinomenine, has exhibited antinociceptive efficacy against various pain models and may become a novel drug candidate for pain management. However, no reported analytical method for quantification of N- Demethylsinomenine in a biological matrix is currently available, and the pharmacokinetic properties of N- Demethylsinomenine are unknown. In the present study, an ultra-high performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS) method for quantification of N- Demethylsinomenine in rat plasma was developed and utilized to examine the preclinical pharmacokinetic profiles of N- Demethylsinomenine. The liquid-liquid extraction using ethyl acetate as the extractant was selected to treat rat plasma samples. The mixture of 25% aqueous phase (0.35% acetic acid-10 mM ammonium acetate buffer) and 75% organic phase (acetonitrile) was chosen as the mobile phases flowing on a ZORBAX C18 column to perform the chromatographic separation. After a 6-min rapid elution, NDSM and its internal standard (IS), metronidazole, were separated successfully. The ion pairs of 316/239 and 172/128 were captured for detecting N- Demethylsinomenine and IS, respectively, using multiple reaction monitoring (MRM) under a positive electrospray ionization (ESI) mode in this mass spectrometry analysis. The standard curve met linear requirements within the concentration range from 3 to 1000 ng/mL, and the lower limit of quantification (LLOQ) was 3 ng/mL. The method was evaluated regarding precision, accuracy, recovery, matrix effect, and stability, and all the results met the criteria presented in the guidelines for validation of biological analysis method. Then the pharmacokinetic profiles of N- Demethylsinomenine in rat plasma were characterized using this validated UPLC-MS/MS method. N- Demethylsinomenine exhibited the feature of linear pharmacokinetics after intravenous (i.v.) or intragastric (i.g.) administration in rats. After i. v. bolus at three dosage levels (0.5, 1, and 2 mg/kg), N- Demethylsinomenine showed the profiles of rapid elimination with mean half-life (T1/2Z) of 1.55-1.73 h, and extensive tissue distribution with volume of distribution (VZ) of 5.62-8.07 L/kg. After i. g. administration at three dosage levels (10, 20, and 40 mg/kg), N- Demethylsinomenine showed the consistent peak time (Tmax) of 3 h and the mean absolute bioavailability of N- Demethylsinomenine was 30.46%. These pharmacokinetics findings will aid in future drug development decisions of N- Demethylsinomenine as a potential candidate for pain analgesia.

Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.

Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude.

Safety assessment of highly automated driving systems in test tracks: A new framework.

Among the three major safety assessment methods (i.e., simulation, test track, and on-road test) for highly automated driving systems (ADS), test tracks provide high fidelity and a safe and controllable testing environment. However, due to the lack of realistic background traffic, scenarios that can be tested in test tracks are usually static and limited. To address this limitation, a new safety assessment framework is proposed in this paper, which integrates an augmented reality (AR) testing platform and a testing scenario library generation (TSLG) method. The AR testing platform generates simulated background traffic in test tracks, which interact with subject ADS under test, to create a realistic traffic environment. The TSLG method can systematically generate a set of critical scenarios under each operational design domain (ODD) and the critical scenarios generated from the TSLG method can be imported into the AR testing platform. The proposed framework has been implemented in the Mcity test track at the University of Michigan with a Level 4 ADS. Field test results show that the proposed framework can accurately and efficiently evaluate the safety performance of highly ADS in a cost-effective fashion. In the cut-in case study, the proposed framework is estimated to accelerate the assessment process by 9.87×104 times comparing to the on-road test approach.