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Indoor air quality (IAQ) in vehicles can be important to people's health, especially for those whose occupations require them to spend extensive time in vehicles. To date, research on vehicle IAQ has primarily focused on direct emissions as opposed to chemistry happening in vehicle cabins. In this work, we conducted time-resolved measurements of the oxidants and oxidant precursors ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), and nitrous acid (HONO) inside the cabin of a 2012 Toyota Rav4 under varying ventilation conditions (i.e., car off, car on with passive ventilation, car on with mechanical ventilation via the recirculating fan, and car on with mechanical ventilation via the direct fan). Ozone levels inside the vehicle were significantly lower than outdoors under most conditions, and were approximately half the outdoor levels when the direct fan was in operation. Nitric oxide and NO2 concentrations were very low both inside the vehicle and outdoors. Nitrous acid levels in the vehicle were lower than reported values in other indoor environments, though much higher than expected outdoor levels. We also investigated the potential for photochemical production of radicals in the vehicle. Time- and wavelength-resolved solar irradiance spectra were collected, and steady state hydroxyl radical (OH) and nitrate radical (NO3) concentrations were calculated. Steady state OH concentrations were predicted to be similar to those in air masses in residences illuminated by sunlight, suggesting the importance of HONO photolysis in vehicles. Conversely, nitrate radicals (NO3) were not considered significant indoor oxidants in our study due to rapid titration by NO. Overall, our findings emphasize the importance of both air exchange and photochemistry in shaping the composition of air inside vehicles.

In 2022, 1,105 bicyclists were killed and 46,195 were injured in motor vehicle crashes in the U.S. Automatic emergency braking (AEB) systems that automatically apply the vehicle brakes can avoid or mitigate bicyclist crashes, but the feature is not evaluated or required in the U.S. This study characterized police-reported vehicle-to-bicycle crashes and assessed speed reductions by two AEB systems in controlled tests replicating common crash vehicle-bicycle scenarios. From 2018 to 2022, a total of 159,474 police-reported and 3,305 fatal crashes involving one passenger vehicle and one bicyclist were extracted from the Crash Report Sampling System and Fatality Analysis Reporting System. Crash configuration and pre-impact movement were coded using the Crash Type variable descriptions. Estimated speeds from 93 crashes were extracted from the Vulnerable Road User Injury Prevention Alliance (VIPA) database. AEB system performance in the 2023 Toyota RAV4 and 2023 Subaru Forester was evaluated when approaching a 15 km/h crossing bicycle target at 40, 50, and 60 km/h during the day and when approaching a slower-moving bicycle target ahead at 50, 60, and 70 km/h during the day and night. Crossing path crashes accounted for 56% of police-reported vehicle-to-bicycle crashes. The estimated travel speed in VIPA crossing path crashes was most frequently 0-20 km/h for the vehicle and 0-17 km/h for the bicycle. Parallel path crashes accounted for 57% of fatal vehicle-to-bicycle crashes. The estimated speed in VIPA parallel path crashes most frequently was 65 km/h or higher for the vehicle and 0-17 km/h for the bicycle. In controlled evaluations, the AEB systems completely or nearly avoided the bicycle target in the crossing path scenarios during daytime and in parallel path scenarios during daytime and at night with high beams. However, the AEB systems only marginally reduced vehicle speed in the parallel path scenarios at night with low beams. Current AEB systems mitigated crossing and parallel path bicycle crashes during the day but not when approaching a bicycle from behind at night with low beams. AEB systems that perform well in every crossing path and parallel path scenario could address 28,600 police-reported and 600 fatal bicycle crashes each year.

Measuring greenhouse gas (GHG) source emissions provides data for validation of GHG inventories, which provide the foundation for climate change mitigation. Two Toyota RAV4 electric vehicles were outfitted with high-precision instrumentation to determine spatial and temporal resolution of GHGs (e.g., nitrous oxide, methane [CH4], and carbon dioxide [CO2]), and other gaseous species and particulate metrics found near emission sources. Mobile measurement platform (MMP) analytical performance was determined over relevant measurement time scales. Pollutant residence times through the sampling configuration were measured, ranging from 3 to 11 sec, enabling proper time alignment for spatial measurement of each respective analyte. Linear response range for GHG analytes was assessed across expected mixing ratio ranges, showing minimal regression and standard error differences between 5, 10, 30, and 60 sec sampling intervals and negligible differences between the two MMPs. GHG instrument drift shows deviation of less than 0.8% over a 24-hr measurement period. These MMPs were utilized in tracer-dilution experiments at a California landfill and natural gas compressor station (NGCS) to quantify CH4 emissions. Replicate landfill measurements during October 2009 yielded annual CH4 emissions estimates of 0.10±0.01, 0.11±0.01, and 0.12±0.02 million tonnes of CO2 equivalent (MTCO2E). These values compare favorably to California GHG Emissions Inventory figures for 2007, 2008, and 2009 of 0.123, 0.125, and 0.126 MTCO2E/yr, respectively, for this facility. Measurements to quantify NGCS boosting facility-wide emissions, during June 2010 yielded an equivalent of 5400±100 TCO2E/yr under steady-state operation. However, measurements during condensate transfer without operational vapor recovery yield an instantaneous emission rate of 2-4 times greater, but was estimated to only add 12 TCO2E/yr overall. This work displays the utility for mobile GHG measurements to validate existing measurement and modeling approaches, so emission inventory values can be confirmed and associated uncertainties reduced. Measuring greenhouse gas (GHG) source emissions provides data and validation for GHG inventories, the foundation for climate change mitigation. Mobile measurement platforms with robust analytical instrumentation completed tracer-dilution experiments in California at a landfill and natural gas compressor station (NGCS) to quantify CH4 emissions. Data collected for landfill CH4 agree with the current California emissions inventory, while NGCS data show the possible variability from this type of facility. This work displays the utility of mobile GHG measurements to validate existing measurement and modeling approaches, such that emission inventory values can be confirmed, associated uncertainties reduced, and mitigation efforts quantified.

The authors show a surgical technique of trapping/resection of ruptured dominant vertebral artery aneurysm in conjunction with reconstruction of vertebral artery by V3-radial artery (RA) graft-V4 bypass through suboccipital craniotomy and far lateral approach. Step by step muscle dissection in posterior fossa enable fine exposure of occipital artery for possible OA-PICA bypass and V3 portion of vertebral artery. Extradural drilling of posterior one-third condyle and condylar fossa facilitate exposure of triangular surgical corridor made by medulla, spinal root of 11th nerve and lower cranial nerves, and thus enabling aneurismal resection and RA-V4 anastomosis. The video can be found here: http://youtu.be/LxsARGdHSVw .

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in

This paper introduces a novel approach that seeks a middle ground for traffic control in multi-lane congestion, where prevailing traffic speeds are too fast, and speed recommendations designed to dampen traffic waves are too slow. Advanced controllers that modify the speed of an automated car for wave-dampening, eco-driving, or other goals, typically are designed with forward collision safety in mind. Our approach goes further, by considering how dangerous it can be for a controller to drive so slowly relative to prevailing traffic that it creates a significant issue for safety and comfort. This paper explores open-road scenarios where large gaps between prevailing speeds and desired speeds can exist, specifically when infrastructure-based variable speed limit systems are not strictly followed at all times by other drivers. Our designed, implemented, and deployed algorithm is able to follow variable speed limits when others also follow it, avoid collisions with vehicles ahead, and adapt to prevailing traffic when other motorists are traveling well above the posted speeds. The key is to reject unsafe speed recommendations from infrastructure-based traffic smoothing systems, based on

In this paper, we present our brand-new platform for Automated Driving research. The chosen vehicle is a RAV4 hybrid SUV from TOYOTA provided with exteroceptive sensors such as a multilayer LIDAR, a monocular camera, Radar and GPS; and proprioceptive sensors such as encoders and a 9-DOF IMU. These sensors are integrated in the vehicle via a main computer running ROS1 under Linux 20.04. Additionally, we installed an open-source ADAS called Comma Two, that runs Openpilot to control the vehicle. The platform is currently being used to research in the field of autonomous vehicles, human and autonomous vehicles interaction, human factors, and energy consumption.

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