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Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position).

Connected and automated technologies poised to change the way vehicles operate are starting to enter the mainstream market. Methods to accurately evaluate these technologies, in particular for their impact on safety and energy, are complex due to the influence of static and environmental factors, such as road environment and traffic scenarios. Therefore, it is important to develop modeling and testing frameworks that can support the development of complex vehicle functionalities in a realistic environment. Microscopic traffic simulations have been increasingly used to assess the performance of connected and automated vehicle technologies in traffic networks. In this paper, we propose and apply an evaluation method based on a combination of microscopic traffic simulation (AIMSUN) and a chassis dynamometer-based vehicle-in-the-loop environment, developed at Argonne National Laboratory.

Active transmission warm-up systems are used by automotive manufacturers in effort to increase powertrain efficiency and decrease fuel consumption. These systems vary from one manufacturer to another, but their main goal is to capture waste heat from the powertrain and accelerate transmission fluid warm-up. In this study, the fuel consumption benefit from the active transmission warm-up system in a 2013 Ford Taurus 2.0 L EcoBoost is quantified on a cold start UDDS drive cycle at ambient temperatures of −7 and 21 °C. In addition to this, the fuel consumption and greenhouse gas emissions impact on the EPA 5-cycle test, hot start HWY drive cycle, and a cold start, constant speed drive cycle is also quantified. An extra effort to determine the maximum possible benefit of active transmission warm-up is made by modifying the test vehicle to provide external heating to pre-heat and further accelerate the transmission fluid warm-up.

This work details two approaches for evaluating transmission warming technology: experimental dynamometer testing and development of a simplified transmission efficiency model to quantify effects under varied real world ambient and driving conditions. Two vehicles were used for this investigation: a 2013 Ford Taurus and a highly instrumented 2011 Ford Fusion (Taurus and Fusion). The Taurus included a production transmission warming system and was tested over hot and cold ambient temperatures with the transmission warming system enabled and disabled. A robot driver was used to minimize driver variability and increase repeatability. Additionally the instrumented Fusion was tested cold and with the transmission pre-heated prior to completing the test cycles. These data were used to develop a simplified thermally responsive transmission model to estimate effects of transmission warming in real world conditions.