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Development and demonstration of a class 6 range-extended electric vehicle for commercial pickup and delivery operation

Cummins Inc.-John Kresse, Ke Li, Jesse Dalton
National Renewable Energy Laboratory-Matthew A. Jeffers, Eric Miller, Kenneth Kelly
  • Technical Paper
  • 2020-01-0848
To be published on 2020-04-14 by SAE International in United States
Range-extended hybrids are an attractive option for medium- and heavy-duty (M/HD) commercial vehicle fleets because they offer the efficiency of an electrified powertrain and accessories with the range of a conventional diesel powertrain. The vehicle essentially operates as if it was purely electric for most trips, while ensuring that all commercial routes can be completed in any weather conditions or geographic terrain. Fuel use and point-source emissions can be significantly reduced, and in some cases eliminated, as many shorter routes can be fully electrified with this architecture. Under a U.S. Department of Energy award for M/HD Vehicle Powertrain Electrification, Cummins has developed a plug-in hybrid electric (PHEV) class 6 truck with a range-extending engine designed for pickup and delivery application. The National Renewable Energy Laboratory (NREL) assisted by developing a representative work day drive cycle for class 6 operation and adapting it to enable track testing. A novel, automated driving system was developed and utilized by Southwest Research Institute (SwRI) to improve the repeatability of vehicle track testing used to quantify vehicle energy consumption. Cummins…
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Investigate partial cabin air re-circulation strategy to improve HVAC system's heating performance using 1D simulation

FCA Engineering India Pvt., Ltd.-Saurabh Belsare, Prakashbabuji Danapalan, Saravanan Sambandan
FCA US LLC-Murali Govindarajalu
  • Technical Paper
  • 2020-01-0159
To be published on 2020-04-14 by SAE International in United States
In cold weather conditions, cabin heating performance is critical for retaining the thermal comfort. Heat is absorbed from the engine by circulating coolant through the engine water jacket and same will be rejected by the heater core. A variable speed blower is used to transfer heat from the heater core to the passenger compartment through floor ducts. The time taken to achieve comfortable cabin temperature determines the performance and capacity of heating ventilating and air conditioning (HVAC) system. In current automotive field, the engine options are provided to customers to meet their needs on the same vehicle platforms. Hence few engine variants cannot warm the cabin up to customer satisfaction. To improve the existing warm up performance of system, Positive thermal coefficient heater (PTC), electric coolant PTC heater, auxiliary pump etc. can be used which increases the overall cost of the vehicle. During warm-up, HVAC system operates in 100% fresh mode. In this study, Partial cabin re-circulation is investigated to understand the effect on the cabin warm-up. In order to demonstrate this phenomenon, a one…
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Understanding How Rain Affects Semantic Segmentation Algorithm Performance

Mississippi State Univ-John Ball
Mississippi State University-Suvash Sharma, Chris Goodin, Matthew Doude, Christopher Hudson, Daniel Carruth, Bo Tang
  • Technical Paper
  • 2020-01-0092
To be published on 2020-04-14 by SAE International in United States
Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this paper, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two synthetic datasets. The first dataset is a pure synthetic rainy dataset which considers the rain droplets on a camera lens, which is suitable for an autonomous vehicle with outside-mounted cameras. This data is generated by the MAVS simulator. In the second dataset, we take good-weather imagery and artificially incorporate rain streaks. By investigating different simulated rain rates, we quantify the performance of such…
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Sensitivity Analysis of Aerodynamic Drag Coefficient to EPA Coastdown Ambient Condition Variation

FCA US LLC-Todd Lounsberry, John Tripp, Gregory Fadler
  • Technical Paper
  • 2020-01-0666
To be published on 2020-04-14 by SAE International in United States
The test cycle average drag coefficient is examined for the variation of allowable EPA coastdown ambient conditions. Coastdown tests are ideally performed with zero wind and at SAE standard conditions. However, often there is some variability in actual ambient weather conditions during testing, and the range of acceptable conditions is further examined in detail as it pertains to the effect on aerodynamic drag derived from the coastdown data. In order to “box” the conditions acceptable during a coastdown test, a sensitivity analysis was performed for wind averaged drag ((CDW ) ̅) as well as test cycle averaged drag coefficients (CDWC) for the fuel economy test cycles. Test cycle average drag for average wind speeds up to 16 km/h and temperatures ranging from 5C to 35C, along with variation of barometric pressure and relative humidity are calculated. The significant effect of ambient cross winds on coastdown determined drag coefficient is demonstrated.
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Investigation of Transient Aerodynamic Effects on Public Highways in Comparison to Individual Driving Situations on a Test Site

FKFS-Felix Wittmeier, Andreas Wagner, Jochen Wiedemann
German Aerospace Center (DLR)-Henning Wilhelmi, Andreas Dillmann
  • Technical Paper
  • 2020-01-0670
To be published on 2020-04-14 by SAE International in United States
Natural wind, roadside obstacles, terrain roughness, and traffic can influence the incident flow of a vehicle driven on public roads. These on-road conditions differ from the idealized statistical steady-state flow environment utilized in CFD simulations and wind tunnel experiments. To understand these transient on-road conditions better, measurements were taken on a test site and on German Autobahn, resulting in the characterization of the transient aerodynamic effects around a vehicle. A compact car was equipped with a measurement system that is capable of determining the transient airflow around the vehicle and the vehicle’s actual driving state. This vehicle was driven several times on a fixed route to investigate different traffic densities on public highways in southern Germany. The tests were conducted under consistent weather conditions and average wind velocities of 2-5 m/s. During the tests the transient incident flow and pressure distribution on the vehicle surface were measured. With the same vehicle, individual driving situations were recreated on a test site. This paper presents a comparison of the aerodynamic characteristics measured by the vehicle during a…
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Reference test system for machine vision used for ADAS functions

Texas A&M University-Abhishek Nayak, Sivakumar Rathinam, Adam Pike, Swaminathan Gopalswamy
  • Technical Paper
  • 2020-01-0096
To be published on 2020-04-14 by SAE International in United States
LDW and LKA systems have the potential to prevent or mitigate 483,000 crashes in the United States every year which includes 87,000 nonfatal injury crashes and 10,345 fatal crashes. Studies have shown that fatalities due to unintentional roadway departures can be significantly reduced if Lane Departure Warning (LDW) and Lane Keep Assist (LKA) systems are used effectively. While LDW and LKA technologies are available, there has been low customer acceptance and penetration of these technologies. These deficiencies can be traced to the inability of many of the perception systems to consistently recognize lane markings and localize the vehicle with respect to the lane marking in a real-world environment of variable markings, changing weather and occlusions. These challenges translate to (i) inconsistent lane detection; (ii) misidentification of lane markings; and (iii) the inability of the systems to locate lane markings in some conditions. Currently, there is no available standard or benchmark to evaluate the quality of either the lane markings or the perception algorithms. This project seeks to establish a reference test system that could be…
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ROBUST SENSOR FUSED OBJECT DETECTION USING CONVOLUTIONAL NEURAL NETWORKS FOR AUTONOMOUS VEHICLES

Kettering University-Jungme Park, Sriram Jayachandran Raguraman, Aakif Aslam, Shruti Gotadki
  • Technical Paper
  • 2020-01-0100
To be published on 2020-04-14 by SAE International in United States
Nowadays, the proliferation of research on the autonomous vehicles and the Advanced Driver Assistance System (ADAS) has resulted from the need for intelligent and safer mobility. Environmental perception is considered as an essential module for autonomous driving and ADAS. In the object detection problem, deep Convolutional Neural Networks (CNNs) become the State-of-the-Art with various different architectures. However, the performances of the existing CNNs have dropped when detecting small objects in distance. To deploy the environmental perception system in real world applications, it is important that the perception system achieves the high accuracy regardless the obstacle sizes, the distances, and weather conditions. In this paper, a sensor fused system for object detection, tracking and classification is proposed by utilizing the advantages of both vision sensor and automotive radar sensor. Data from on-vehicle radar sensor and camera sensor are processed in real time simultaneously. The proposed system consists of three modules: 1) the Coordinate Conversion module converts the radar coordinates into the image coordinate system. 2) Multi Level-Multi Region detection system based on the deep CNNs. The…
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In-situ studies on the effect of solar control glazings on in-cabin thermal environment for hot and humid climatic zones

Saint Gobain India Pvt., Ltd.-Jyothi Latha Tamalapakula
  • Technical Paper
  • 2020-01-1257
To be published on 2020-04-14 by SAE International in United States
Thermal comfort in a passenger cabin is the basic necessity of an occupant especially in hot and humid climatic conditions. It is known that the reflective glazing solutions provide better thermal comfort inside the cabin due to significant reflection of IR part of solar radiation. However, in hot and humid climatic zones like India, significant reduction in heat load can also be achieved through cost effective solar control absorbing glazings. Thus, the present work aims to study the effect of solar control absorbing glazings on cabin inside temperatures and its impact on thermal comfort of the occupants in tropical climates. A combination of glazing sets with a range of solar energy transmission and absorption values are considered for the study. Indoor soak and cool tests are performed on a sedan model with multiple sets of solar control absorbing glass combinations. A constant ambient temperature of 38˚C and solar radiation of 1000W/m2 are maintained throughout the tests. Wind speed was simulated for a vehicle running speed of 45kmph. Net heat gain inside the cabin as a…
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LiDAR and Camera-based Convolutional Neural Network Detection for Autonomous Driving

National Research Council Canada-Ismail Hamieh, Ryan Myers, Hisham Nimri, Taufiq Rahman
University of Windsor-Aarron Younan, Brad Sato, Abdul El-Kadri, Selwan Nissan, Kemal Tepe
  • Technical Paper
  • 2020-01-0136
To be published on 2020-04-14 by SAE International in United States
Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. Object classification and detection are crucial tasks that need to be solved accurately and robustly in order to achieve higher automation levels. Current approaches for classification and detection use either cameras or light detection and ranging (LiDAR) sensors. Cameras can work at high frame-rate, and provide dense information over a long range under good illumination and fair weather. LiDARs scan the environment by using their own emitted pulses of laser light and they are only marginally affected by the external lighting conditions. LiDARs provide accurate distance measurements. However, they have a limited range, typically between 10 and 100 m, and provide sparse data. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw camera and LiDAR data, several basic statistics such as elevation and density are generated. The system leverages simple and fast convolutional neural network (CNN) solution for object classification and…
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Effect of Adherent Rain on the Performance of Image-Based Algorithms in Automotive Domain

University of Michigan - Dearborn-Yazan Hamzeh, Zaid El-Shair, Samir A. Rawashdeh
  • Technical Paper
  • 2020-01-0104
To be published on 2020-04-14 by SAE International in United States
Adverse weather conditions degrade the quality of images used in vision-based advanced driver assistance systems (ADAS) and autonomous driving algorithms. Adherent rain drops onto a vehicle’s windshield occlude parts of the input image and blur background texture in regions covered by them. Rain also changes image intensity and disturbs chromatic properties of color images. In this work, we collected a dataset using a camera mounted behind a windshield at different rain intensities. The data was processed to generate a set of distorted images by adherent raindrops along with ground truth data of clear images (just after a windshield wipe). We quantitatively evaluated the amount of distortion caused by the raindrops, using the Normalized Cross-Correlation (NCC) and structural similarity (SSIM) methods. While most prior work in the field of rain detection and removal focuses on the image restoration aspects, they typically do not provide quantitative measures to the effect of degradation of input image quality on the performance of image-based algorithms. We quantitatively evaluated the effect of raindrop distortion on deep-learning-based object detection algorithms by comparing…