Reducing Detailed Vehicle Energy Dynamics to Physics-Like Models

The energy demand of vehicles, particularly in unsteady drive cycles, is affected by complex dynamics internal to the engine and other powertrain components. Yet, in many applications, particularly macroscopic traffic flow modeling and optimization, structurally simple approximations to the complex vehicle dynamics are needed that nevertheless reproduce the correct effective energy behavior. This work presents a systematic model reduction pipeline that starts from complex vehicle models based on the Autonomie software and derives a hierarchy of simplified models that are fast to evaluate, easy to disseminate in open-source frameworks, and compatible with optimization frameworks. The pipeline, based on a virtual chassis dynamometer and subsequent approximation strategies, is reproducible and is applied to six different vehicle classes to produce concrete explicit energy models that represent an average vehicle in each class and leverage the accuracy and validation work of the Autonomie software.Comment: 40 pages, 9 figure

Inverse Correlation between Stress and Adaptive Coping in Medical Students

BACKGROUND: Medical students in their academic years are generally under stress but very few studies revealed the relationship between the stress and how the students manage to adapt these stressful conditions. AIM: The aim of the study was to investigate the levels of stress and their adaptive coping in the 1st 3 years medical students and also to determine the factors associated with adaptive coping strategies. METHODS: This is a descriptive cross-sectional study conducted on 441 medical students of Qassim University from September-October 2019. First 3 years medical students were randomly selected and their stress levels or adaptive coping strategies were determined by general health questionnaire (GHQ-12) and strategies coping mechanisms (SCM), respectively. The 5-points Likert scale was used for scoring and the data obtained were further validated by DASS and Brief COPE scales. RESULTS: Out of 441 medical students, 39.2% agreed to participate. The data showed that the level of stress among students was highest during their 1st year academic blocks, followed by 2nd and 3rd year students. Interesting, the adaptive coping among them was found highest during the academic blocks of 3rd year students, followed by the 2nd and 1st year students. Importantly, female students showed better adaptation against stress. Students living with their parents avoided stress in better ways as compared to those who were living alone. CONCLUSION: This is the first study that shows an inverse correlation between the stress and adaptive coping in medical students of Qassim University. The data concluded that adaptation of stress in the 3rd-year students was the highest followed by 2nd and 1st year medical students. Moreover, female students adapted well against stress and students living alone showed worse adaptation of stress

Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs

The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,"are a significant source of wasted energy. Toward this goal, the CIRCLES project designed a control system referred to as the MegaController by the CIRCLES team, that could be deployed in real traffic. Our field experiment leveraged a heterogeneous fleet of 100 longitudinally-controlled vehicles as Lagrangian traffic actuators, each of which ran a controller with the architecture described in this paper. The MegaController is a hierarchical control architecture, which consists of two main layers. The upper layer is called Speed Planner, and is a centralized optimal control algorithm. It assigns speed targets to the vehicles, conveyed through the LTE cellular network. The lower layer is a control layer, running on each vehicle. It performs local actuation by overriding the stock adaptive cruise controller, using the stock on-board sensors. The Speed Planner ingests live data feeds provided by third parties, as well as data from our own control vehicles, and uses both to perform the speed assignment. The architecture of the speed planner allows for modular use of standard control techniques, such as optimal control, model predictive control, kernel methods and others, including Deep RL, model predictive control and explicit controllers. Depending on the vehicle architecture, all onboard sensing data can be accessed by the local controllers, or only some. Control inputs vary across different automakers, with inputs ranging from torque or acceleration requests for some cars, and electronic selection of ACC set points in others. The proposed architecture allows for the combination of all possible settings proposed above. Most configurations were tested throughout the ramp up to the MegaVandertest

Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions

Passenger and heavy-duty vehicles make up 36% of California’s greenhouse gas (GHG) emissions. Reducing emissions from vehicular travel is therefore paramount for any path towards carbon neutrality. Efforts to reduce GHGs by encouraging mode shift or increasing vehicle efficiency are, and will continue to be, a critical part of decarbonizing the transportation sector. Emerging technologies are creating an opportunity to reduce GHGs. Human driving behaviors in congested traffic have been shown to create stop-and-go waves. When waves form, cars periodically slow down (sometimes to a stop) and then speed back up again; this repeated braking and accelerating leads to higher fuel consumption, and correspondingly increasingly GHG emissions. Flow smoothing, or the use of a specially designed adaptive cruise controllers to dissipate these waves, can reduce fuel consumption of all the cars on the road. By keeping all vehicles at a constant speed, flow smoothing can minimize system-wide GHG emissions. This report presents the results of flow-smoothing when used in simulation, discusses current work on implementing flow-smoothing in real world-highways, and presents policy discussions on how to support flow smoothing

Automated Vehicle Technology Has the Potential to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions

In an ideal world, all cars along a congested roadway would travel at the same constant average speed; however, this is hardly the case. As soon as one driver brakes, trailing cars must also brake to compensate, leading to “stop and go” traffic waves. This unnecessary braking and accelerating increases fuel consumption (and greenhouse gas emissions) by as much as 67 percent.1 Fortunately, automated vehicles (AVs) — even Level 2 AVs2 which are commercially available today — have the potential to mitigate this problem. By accelerating less than a human would, an AV with flow smoothing technology is able to smooth out a traffic wave, eventually leading to free-flowing traffic (See Figure 1). To demonstrate the potential of flow smoothing on reducing greenhouse gas emissions, researchers at UC Berkeley used a calibrated model of the I-210 freeway in Los Angeles to simulate and measure the effect of deploying different percentages (10%, 20%, 30%) of flow-smoothing AVs on the average miles per gallon (MPG) of non-AVs in the traffic system

Using Automated Vehicle (AV) Technology to Smooth Traffic Flow and Reduce Greenhouse Gas Emissions

UC-ITS-2021-26Passenger and heavy-duty vehicles make up 36% of California\u2019s greenhouse gas (GHG) emissions. Reducing emissions from vehicular travel is therefore paramount for any path towards carbon neutrality. Efforts to reduce GHGs by encouraging mode shift or increasing vehicle efficiency are, and will continue to be, a critical part of decarbonizing the transportation sector. Emerging technologies are creating an opportunity to reduce GHGs. Human driving behaviors in congested traffic have been shown to create stop-and-go waves. When waves form, cars periodically slow down (sometimes to a stop) and then speed back up again; this repeated braking and accelerating leads to higher fuel consumption, and correspondingly increasingly GHG emissions. Flow smoothing, or the use of a specially designed adaptive cruise controllers to dissipate these waves, can reduce fuel consumption of all the cars on the road. By keeping all vehicles at a constant speed, flow smoothing can minimize system-wide GHG emissions. This report presents the results of flow-smoothing when used in simulation, discusses current work on implementing flow-smoothing in real world-highways, and presents policy discussions on how to support flow smoothing

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

International audienceFigure 1. Traffic waves generated by human driving increase the energy consumption of traffic flow. A small fraction of well-controlled automated vehicles can smooth the flow and the reduce energy consumption