Multi-agent traffic simulations to estimate the impact of automated technologies on safety.

Objective: The objective of this article was to develop a multi-agent traffic simulation methodology to estimate the potential road safety improvements of automated vehicle technologies. Methods: We developed a computer program that merges road infrastructure data with a large number of vehicles, drivers, and pedestrians. Human errors are induced by modeling inattention, aimless driving, insufficient safety confirmation, misjudgment, and inadequate operation. The program was applied to simulate traffic in a prescribed area in Tsukuba city. First, a 100% manual driving scenario was set to simulate traffic for a total preset vehicle travel distance. The crashes from this simulation were compared with real-world crash data from the prescribed area from 2012 to 2017. Thereafter, 4 additional scenarios of increasing levels of automation penetration (including combinations of automated emergency braking [AEB], lane departure warning [LDW], and SAE Level 4 functions) were implemented to estimate their impact on safety. Results: Under manual driving, the system simulated a total of 859 crashes including single-car lane departure, car-to-car, and car-to-pedestrian crashes. These crashes tended to occur in locations similar to real-world crashes. The number of crashes predicted decreased to 156 cases with increasing level of automation. All of the technologies considered contributed to the decrease in crashes. Crash reductions attributable to AEB and LDW in the simulations were comparable to those reported in recent field studies. For the highest levels of automation, no assessment data were available and hence the results should be carefully treated. Further, in modeling automated functions, potentially negative aspects such as sensing failure or human overreliance were not incorporated. Conclusions: We developed a multi-agent traffic simulation methodology to estimate the effect of different automated vehicle technologies on safety. The crash locations resulting from simulations of manual driving within a limited area in Japan were preliminary assessed by comparison with real-world crash data collected in the same area. Increasing penetration levels of AEB and LDW led to a large reduction in both the frequency and severity of rear-end crashes, followed by car-to-car head-on crashes and single-vehicle lane departure crashes. Preliminary estimations of the potential safety improvements that may be achieved with highly automated driving technologies were also obtained.

SMC6 is required for MMS-induced interchromosomal and sister chromatid recombinations in Saccharomyces cerevisiae.

SMC6 (RHC18) in Saccharomyces cerevisiae, which is a homologue of the Schizosaccharomyces pombe rad18+ gene and essential for cell viability, encodes a structural maintenance of chromosomes (SMC) family protein. In contrast to the rest of the SMC family of proteins, Smc1-Smc4, which are the components of cohesin or condensin, little is known about Smc6. In this study, we generated temperature sensitive (ts) smc6 mutants of budding yeast and characterized their properties. One ts-mutant, smc6-56, ceased growth soon after up-shift to a non-permissive temperature, arrested in the late S and G2/M phase, and gradually lost viability. smc6-56 cells at a permissive temperature showed a higher sensitivity than wild-type cells to various DNA damaging agents including methyl methanesulfonate (MMS). The rad52 smc6-56 double mutant showed a sensitivity to MMS similar to that of the rad52 single mutant, indicating that Smc6 is involved in a pathway that requires Rad52 to function. Moreover, no induction of interchromosomal recombination and sister chromatid recombination was observed in smc6-56 cells, which occurred in wild-type cells upon exposure to MMS.