scholarly journals Estimating Bounds of Aerodynamic, Mass, and Auxiliary Load Impacts on Autonomous Vehicles: A Powertrain Simulation Approach

2021 ◽  
Vol 13 (22) ◽  
pp. 12405
Author(s):  
Yuche Chen ◽  
Ruixiao Sun ◽  
Xuanke Wu
Keyword(s):  
Autonomous Vehicles ◽  
Steering Wheel ◽  
Power Demand ◽  
Major Power ◽  
Vehicle Performance ◽  
Design Changes ◽  
Energy Economy ◽  
Driving Conditions ◽  
Vehicle Automation ◽  
The Impact

Vehicle automation requires new onboard sensors, communication equipment, and/or data processing units, and may encourage modifications to existing onboard components (such as the steering wheel). These changes impact the vehicle’s mass, auxiliary load, coefficient of drag, and frontal area, which then change vehicle performance. This paper uses the powertrain simulation model FASTSim to quantify the impact of autonomy-related design changes on a vehicle’s fuel consumption. Levels 0, 2, and 5 autonomous vehicles are modeled for two battery-electric vehicles (2017 Chevrolet Bolt and 2017 Nissan Leaf) and a gasoline powered vehicle (2017 Toyota Corolla). Additionally, a level 5 vehicle is divided into pessimistic and optimistic scenarios which assume different electronic equipment integration format. The results show that 4–8% reductions in energy economy can be achieved in a L5 optimistic scenario and an 10–15% increase in energy economy will be the result in a L5 pessimistic scenario. When looking at impacts on different power demand sources, inertial power is the major power demand in urban driving conditions and aerodynamic power demand is the major demand in highway driving conditions.

scholarly journals Semi-Autonomous Vehicles as a Cognitive Assistive Device for Older Adults

Geriatrics ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 63
Author(s):  
Frank Knoefel ◽  
Bruce Wallace ◽  
Rafik Goubran ◽  
Iman Sabra ◽  
Shawn Marshall
Keyword(s):  
Older Adults ◽  
Autonomous Vehicles ◽  
Cognitive Change ◽  
Health Needs ◽  
Assistive Device ◽  
Cognitive Changes ◽  
Age Related ◽  
Vehicle Automation ◽  
The Impact ◽  
Aging Drivers

Losing the capacity to drive due to age-related cognitive decline can have a detrimental impact on the daily life functioning of older adults living alone and in remote areas. Semi-autonomous vehicles (SAVs) could have the potential to preserve driving independence of this population with high health needs. This paper explores if SAVs could be used as a cognitive assistive device for older aging drivers with cognitive challenges. We illustrate the impact of age-related changes of cognitive functions on driving capacity. Furthermore, following an overview on the current state of SAVs, we propose a model for connecting cognitive health needs of older drivers to SAVs. The model demonstrates the connections between cognitive changes experienced by aging drivers, their impact on actual driving, car sensors’ features, and vehicle automation. Finally, we present challenges that should be considered when using the constantly changing smart vehicle technology, adapting it to aging drivers and vice versa. This paper sheds light on age-related cognitive characteristics that should be considered when developing future SAVs manufacturing policies which may potentially help decrease the impact of cognitive change on older adult drivers.

scholarly journals The iconography of vehicle automation—a focus group study

2021 ◽  
Author(s):  
Joy Richardson ◽  
Kirsten M. A. Revell ◽  
Jisun Kim ◽  
Neville A. Stanton
Keyword(s):  
Focus Group ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
International Markets ◽  
Cultural Barriers ◽  
Focus Group Study ◽  
Complex Information ◽  
Vehicle Automation ◽  
The Impact ◽  
Level 2

AbstractSAE level 2 and 3 semi-autonomous vehicles are widely available but, due to the nature of automation, their in-vehicle displays are required to communicate more complex information to the driver. Examination of interfaces from a variety of manufacturers revealed a clear lack of consistency in the way key information is displayed. Different manufacturers have adopted icons varying in shape and colour to convey the same message. When driving a semi-autonomous vehicle, mode awareness is critical for trust, performance and safety. Standardisation of icons has been shown to have many benefits including opening products up to wider international markets by helping overcome language and cultural barriers, by providing a method of communication which can surpass them. However, the current lack of standardisation in icon design could cause mode confusion and has little cross-vehicle compatibility. To understand the impact of mode confusion on users, a focus group was held in which participants were asked to interpret the meaning of icons from a variety of different driver interfaces. Ambiguity in user interpretations makes the case for the introduction of new ISO standard icons to better support drivers in SAE level 2 and 3 automated vehicles.

Influence of Lane Width on Semi- Autonomous Vehicle Performance

2020 ◽  
Vol 2674 (9) ◽  
pp. 279-286 ◽  
Author(s):  
Alfredo García ◽  
Francisco Javier Camacho-Torregrosa
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Lateral Control ◽  
Maximum Width ◽  
Vehicle Performance ◽  
Minimum Width ◽  
Road Infrastructure ◽  
Lane Width ◽  
Rapid Deployment ◽  
The Impact

In the medium-term, the number of semi-autonomous vehicles is expected to rise significantly. These changes in vehicle capabilities make it necessary to analyze their interaction with road infrastructure, which has been developed for human-driven vehicles. Current systems use artificial vision, recording the oncoming road and using the center and edgeline road markings to automatically facilitate keeping the vehicle within the lane. In addition to alignment and road markings, lane width has emerged as one of the geometric parameters that might cause disengagement and therefore must be assessed. The objective of this research was to study the impact of lane width on semi-autonomous vehicle performance. The automatic lateral control of this type of vehicle was tested along 81 lanes of an urban arterial comprising diverse widths. Results showed that the semi-autonomous system tended to fail on narrow lanes. There was a maximum width below which human control was always required—referred to as the human lane width—measuring 2.5 m. A minimum width above which automatic control was always possible—the automatic lane width—was established to be 2.75 m. Finally, a lane width of 2.72 m was found to have the same probability of automatic and human lateral control, namely the critical lane width. Following a similar methodology, these parameters could be determined for other vehicles, enhancing the interaction between autonomous vehicles and road infrastructure and thus supporting rapid deployment of autonomous technology without compromising safety.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

scholarly journals Theoretical Studies of the Interaction between Screw Surface and Material in the Mixer

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 962
Author(s):  
Andrzej Marczuk ◽  
Vasily Sysuev ◽  
Alexey Aleshkin ◽  
Petr Savinykh ◽  
Nikolay Turubanov ◽  
...  
Keyword(s):  
Friction Coefficient ◽  
High Efficiency ◽  
Animal Feed ◽  
Structural Features ◽  
Power Demand ◽  
Friction Coefficients ◽  
Length Unit ◽  
Mixing Chamber ◽  
Size Of Particles ◽  
The Impact

Mixing is one of the most commonly used processes in food, animal feed, chemical, cosmetic, etc., industries. It is supposed to provide high-quality homogenous, nutritious mixtures. To provide appropriate mixing of materials while maintaining the process high efficiency and low energy consumption it is crucial to explore and describe the material flow caused by the movement of mixing elements and the contact between particles. The process of mixing is also affected by structural features of the machine components and the mixing chamber, speed of mixing, and properties of the mixed materials, such as the size of particles, moisture, friction coefficients. Thus, modeling of the phenomena that accompany the process of mixing using the above-listed parameters is indispensable for appropriate implementation of the process. The paper provides theoretical power calculations that take into account the material speed change, the impact of the material friction coefficient on the screw steel surface and the impact of the friction coefficient on the material, taking into account the loading height of the mixing chamber and the chamber loading value. Dependencies between the mixer power and the product degree of fineness, rotational speed of screw friction coefficients, the number of windings per length unit, and width of the screw tape have been presented on the basis of a developed model. It has been found that power increases along with an increase in the value of these parameters. Verification of the theoretical model indicated consistence of the predicted power demand with the power demand determined in tests performed on a real object for values of the assumed, effective loading, which was 65–75%.

The Impact of Mental States on Semi-autonomous Driving Takeover Performance: A Systematic Review

2020 ◽  
Vol 64 (1) ◽  
pp. 1372-1376
Author(s):  
Gaojian Huang ◽  
Christine Petersen ◽  
Brandon J. Pitts
Keyword(s):  
Systematic Review ◽  
Autonomous Vehicles ◽  
Response Times ◽  
Autonomous Driving ◽  
Mental States ◽  
Manual Control ◽  
Monitoring Systems ◽  
Wide Range ◽  
Different Types ◽  
The Impact

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

scholarly journals An Efficiency Enhancing Methodology for Multiple Autonomous Vehicles in an Urban Network Adopting Deep Reinforcement Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1514 ◽  
Author(s):  
Quang-Duy Tran ◽  
Sang-Hoon Bae
Keyword(s):  
Reinforcement Learning ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Penetration Rate ◽  
Autonomous Vehicle ◽  
Effective Means ◽  
Urban Network ◽  
Learning Agents ◽  
Policy Optimization ◽  
The Impact

To reduce the impact of congestion, it is necessary to improve our overall understanding of the influence of the autonomous vehicle. Recently, deep reinforcement learning has become an effective means of solving complex control tasks. Accordingly, we show an advanced deep reinforcement learning that investigates how the leading autonomous vehicles affect the urban network under a mixed-traffic environment. We also suggest a set of hyperparameters for achieving better performance. Firstly, we feed a set of hyperparameters into our deep reinforcement learning agents. Secondly, we investigate the leading autonomous vehicle experiment in the urban network with different autonomous vehicle penetration rates. Thirdly, the advantage of leading autonomous vehicles is evaluated using entire manual vehicle and leading manual vehicle experiments. Finally, the proximal policy optimization with a clipped objective is compared to the proximal policy optimization with an adaptive Kullback–Leibler penalty to verify the superiority of the proposed hyperparameter. We demonstrate that full automation traffic increased the average speed 1.27 times greater compared with the entire manual vehicle experiment. Our proposed method becomes significantly more effective at a higher autonomous vehicle penetration rate. Furthermore, the leading autonomous vehicles could help to mitigate traffic congestion.

scholarly journals Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?

2021 ◽  
Vol 18 (11) ◽  
pp. 5567
Author(s):  
Moneim Massar ◽  
Imran Reza ◽  
Syed Masiur Rahman ◽  
Sheikh Muhammad Habib Abdullah ◽  
Arshad Jamal ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Autonomous Vehicles ◽  
Significant Contribution ◽  
Ghg Emissions ◽  
Interval Estimate ◽  
Modal Shift ◽  
Vehicle Mile Travel ◽  
Emission Changes ◽  
Negative Impacts ◽  
The Impact

The potential effects of autonomous vehicles (AVs) on greenhouse gas (GHG) emissions are uncertain, although numerous studies have been conducted to evaluate the impact. This paper aims to synthesize and review all the literature regarding the topic in a systematic manner to eliminate the bias and provide an overall insight, while incorporating some statistical analysis to provide an interval estimate of these studies. This paper addressed the effect of the positive and negative impacts reported in the literature in two categories of AVs: partial automation and full automation. The positive impacts represented in AVs’ possibility to reduce GHG emission can be attributed to some factors, including eco-driving, eco traffic signal, platooning, and less hunting for parking. The increase in vehicle mile travel (VMT) due to (i) modal shift to AVs by captive passengers, including elderly and disabled people and (ii) easier travel compared to other modes will contribute to raising the GHG emissions. The result shows that eco-driving and platooning have the most significant contribution to reducing GHG emissions by 35%. On the other side, easier travel and faster travel significantly contribute to the increase of GHG emissions by 41.24%. Study findings reveal that the positive emission changes may not be realized at a lower AV penetration rate, where the maximum emission reduction might take place within 60–80% of AV penetration into the network.

Altitude Effects on the Performance of Small Radial Turbomachinery: Part I — Compressor Impellers

2016 ◽  
Author(s):  
Dries Verstraete ◽  
Kjersti Lunnan
Keyword(s):  
Reynolds Number ◽  
Performance Analysis ◽  
Rotational Speed ◽  
Pressure Ratio ◽  
Unmanned Aircraft ◽  
Good Match ◽  
One Dimensional ◽  
Design Changes ◽  
Current Article ◽  
The Impact

Small unmanned aircraft are currently limited to flight ceilings below 20,000 ft due to the lack of an appropriate propulsion system. One of the most critical technological hurdles for an increased flight ceiling of small platforms is the impact of reduced Reynolds number conditions at altitude on the performance of small radial turbomachinery. The current article investigates the influence of Reynolds number on the efficiency and pressure ratio of two small centrifugal compressor impellers using a one-dimensional meanline performance analysis code. The results show that the efficiency and pressure ratio of the 60 mm baseline compressor at the design rotational speed drops with 6–9% from sea-level to 70,000 ft. The impact on the smaller 20 mm compressor is slightly more pronounced and amounts to 6–10%. Off-design changes at low rotational speeds are significantly higher and can amount to up to 15%. Whereas existing correlations show a good match for the efficiency drop at the design rotational speed, they fail to predict efficiency changes with rotational speed. A modified version is therefore proposed.

Sign in / Sign up

Export Citation Format

Share Document