Safety-oriented automated vehicle longitudinal control considering both stability and damping behavior.

Extensive research has examined the potential benefits of Automated Vehicles (AVs) for increasing traffic capacity and improving safety. However, previous studies on AV longitudinal control have focused primarily on control stability and instability or tradeoffs between safety and stability, neglecting the importance of vehicle damping characteristics. This study aims to demonstrate the significance of explicitly considering safety in addition to stability in AV longitudinal control through damping behavior analysis. Specifically, it proposes a safety-oriented AV longitudinal control and provides recommendations on the control parameters. For the proposed AV control, an Adaptive Cruise Control (ACC) model is integrated with damping behavior analysis to model AV safety under continuous traffic perturbations. Numerical simulations are conducted to quantify the relationship between mobility and safety for AVs considering both damping behavior and control stability. Different ACC control parameters are evaluated in terms of damping and stability properties, and their safety impacts are assessed based on various surrogate safety measures such as Deceleration Rate to Avoid Crash (DRAC), Crash Potential Index (CPI) and Time-Integrated Time-to-collision (TIT). The results indicate that an underdamped state (ACC damping ratio < 1) is less safe than the critically damped state (ACC damping ratio = 1) and the overdamped state (ACC damping ratio > 1). Furthermore, given the same AV car-following time lag, ACC with a damping ratio between 1 and 1.2 provides better safety performance. Increasing the AV car-following time lag can improve both safety and stability when the remaining ACC control parameters are kept the same. In this case, the optimal safety-oriented ACC regions also increase. The findings of this study provide important insights into designing safe and stable AV longitudinal control algorithms.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety.

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle's (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data.

Traditional statistical crash prediction models oftentimes suffer from poor data quality and require large amount of historical data. In this paper, we propose a crash prediction method based on a bivariate extreme value theory (EVT) framework, considering both drivers' perception-reaction failure and failure to proper evasive actions. An unmanned aerial vehicle (UAV) was utilized to collect videos of ten intersections in Fengxian, China, at representative time periods. High-resolution vehicle trajectory data were extracted by a Kanade-Lucas-Tomasi (KLT) technique, based on four detailed metrics were derived including Time-to-accident (TA), Post-encroachment Time (PET), minimum Time-to-collision (mTTC), and Maximum Deceleration Rate (MaxD). TA was expected to capture the chance of perception-reaction failure, while other three metrics were used to measure the probability of failure to proper evasive actions. Univariate EVT models were applied to obtain marginal crash probability based on each metric. Bivariate EVT models were developed to obtain joint crash probability based on three pairs: TA and mTTC, TA and PET, and TA and MaxD. Thus, union crash probability within observation periods can be derived and the annual crash frequency of each intersection was predicted. The predictions were compared to actual annual crash frequencies, using multiple tests. The findings are three-folds: 1. The best conflict metrics for angle and rear-end crash predictions were different; 2. Bivariate EVT models were found to be superior to univariate models, regarding both angle and rear-end crash predictions; 3. TA appeared to be an important conflict metric that should be considered in a bivariate EVT model framework. In general, the proposed method can be considered as a promising tool for safety evaluation, when crash data are limited.

Analysis of diacylglycerols by ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry: Double bond location and isomers separation.

Diacylglycerols (DAGs) are important lipid intermediates and have been implicated in human diseases. Isomerism complicates their mass spectrometric analysis; in particular, it is difficult to identify fatty acid substituents and locate the double bond positions in unsaturated DAGs. We have developed an analytical strategy using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS) in conjunction with dimethyl disulfide (DMDS) derivatization and collision cross-section (CCS) measurement to characterize DAGs in biological samples. The method employs non-aqueous reversed-phase chromatographic separation and profile collision energy (CE) mode for MS(E) and MS/MS analyses. Three types of fragment ions were produced simultaneously. Hydrocarbon ions (m/z 50-200) obtained at high CE helped to distinguish unsaturated and saturated DAGs rapidly. Neutral loss ions and acylium ions (m/z 300-400) produced at low CE were used to identify fatty acid substituents. Informative methyl thioalkane fragment ions were used to locate the double bonds of unsaturated DAGs. Mono-methylthio derivatives were formed mainly by the reaction of DAGs with DMDS, where methyl thiol underwent addition to the first double bond farthest from the ester terminus of unsaturated fatty acid chains. The addition of CCS values maximized the separation of isomeric DAG species and improved the confidence of DAG identification. Fourteen DAGs were identified in mouse myotube cells based on accurate masses, characteristic fragment ions, DMDS derivatization, and CCS values.

AICAR and Metformin Exert AMPK-dependent Effects on INS-1E Pancreatic ß-cell Apoptosis via Differential Downstream Mechanisms.

The role of AMP-activated protein kinase (AMPK) in pancreatic ß-cell apoptosis is still controversial, and the reasons for the discrepancies have not been clarified. In the current study, we observed the effects of two well-known AMPK activators 5-aminoimidazole-4-carboxamide ribonucleoside (AICAR) and metformin, on apoptosis in rat insulinoma INS-1E cells, and further explored their possible mechanisms. Both AICAR and metformin protected INS-1E cells from palmitate-induced apoptosis, as reflected by decreases in both cleaved caspase 3 protein expression and caspase 3/7 activity, and these protective effects were abrogated by AMPK inhibitor compound C. The protective action of AICAR was probably mediated by the suppression of triacylglycerol accumulation, increase in Akt phosphorylation and decrease in p38 MAPK phosphorylation, while metformin might exert its protective effect on INS-1E cells by decreases in both JNK and p38 MAPK phosphorylation. All these regulations were dependent on AMPK activation. However, under standard culture condition, AICAR increased JNK phosphorylation and promoted INS-1E cell apoptosis in an AMPK-dependent manner, whereas metformin showed no effect on apoptosis. Our study revealed that AMPK activators AICAR and metformin exhibited different effects on INS-1E cell apoptosis under different culture conditions, which might be largely attributed to different downstream mediators. Our results provided new and informative clues for better understanding of the role of AMPK in ß-cell apoptosis.