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Autonomous Vehicle Multi-Sensors Localization in Unstructured Environment

FEV North America Inc.-Qusay Alrousan, Hamzeh Alzu'bi, Andrew Pfeil, Tom Tasky
  • Technical Paper
  • 2020-01-1029
To be published on 2020-04-14 by SAE International in United States
Autonomous driving in unstructured environments is a significant challenge due to the inconsistency of important information for localization such as lane markings. To reduce the uncertainty of vehicle localization in such environments, sensor fusion of LiDAR, Radar, Camera, GPS/IMU, and Odometry sensors is utilized. This paper discusses a hybrid localization technique developed using: LiDAR based Simultaneous Localization and Mapping (SLAM), GPS/IMU and Odometry data, and object lists from Radar and Camera sensors. An Extended Kalman Filter (EKF) is utilized to fuse data from all sensors in two phases. In the preliminary stage, the SLAM-based vehicle coordinates are fused with the GPS-based positioning. The output of this stage is then fused with the objects-based localization. This approach was successfully tested on FEV’s Smart Vehicle Demonstrator at FEV’s HQ representing a complicated test environment with dynamic and static objects. The test results show that multi-sensor fusion improves the vehicle’s localization compared to GPS or LiDAR alone.
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A Smart Measuring System for Vehicle Dynamics Testing

Politecnico di Torino-Enrico Galvagno, Stefano Mauro, Stefano Pastorelli, Antonio Tota
  • Technical Paper
  • 2020-01-1066
To be published on 2020-04-14 by SAE International in United States
A fast measurement of the car handling performance is highly desirable to easily compare and assess different car setup, e.g. tires size and supplier, suspension settings, etc. Instead of the expensive professional equipment normally used by car manufacturers for vehicle testing, the authors propose a low cost solution that is nevertheless accurate enough for comparative evaluations. The paper presents a novel measuring system for vehicle dynamics analysis, which is based uniquely on the sensors embedded in a smartphone and completely independent on the signals available through vehicle CAN bus. Data from tri-axial accelerometer, gyroscope, GPS and camera are jointly used to compute the typical quantities analyzed in vehicle dynamics applications. In addition to signals like yaw rate, lateral and longitudinal acceleration, vehicle speed and trajectory, normally available when working with Inertial Measurement Units (IMU) equipped with GPS, in the present application also the steering wheel angle is measured by artificial vision algorithms that use the phone camera.. The latter signal, besides being important for identifying the maneuver imposed by the driver, it enables the usage…
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Statistical Analysis of City Bus Driving Cycle Features for the Purpose of Multidimensional Driving Cycle Synthesis

University of Zagreb-Jakov Topić, Branimir Skugor, Josko Deur
  • Technical Paper
  • 2020-01-1288
To be published on 2020-04-14 by SAE International in United States
Driving cycles are typically defined as time profiles of vehicle velocity, and as such they reflect basic driving characteristics. They have a wide application from the perspective of both conventional and electric road vehicles, ranging from prediction of fuel/energy consumption (e.g. for certification purposes), estimation of greenhouse gas and pollutant emissions to selection of optimal vehicle powertrain configuration and design of its control strategy. In the case of electric vehicles, the driving cycles are also applied to determine effective vehicle range, battery life period, and charging management strategy. Nowadays, in most applications artificial certification driving cycles are used. As they do not represent realistic driving conditions, their application results in generally unreliable estimates and analyses. Therefore, recent research efforts have been directed towards development of statistically representative synthetic driving cycles derived from recorded GPS driving data. The state-of-the-art synthesis approach is based on Markov chains, typically including vehicle velocity and acceleration as Markov chain states. However, apart from the vehicle velocity and acceleration, a road slope and vehicle mass are also shown to significantly impact…
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Effect of Space Weather on Autonomous Vehicle Navigation

University Of Detroit Mercy-Alan Hoback
  • Technical Paper
  • 2020-01-0140
To be published on 2020-04-14 by SAE International in United States
Autonomous vehicle systems integrate multiple information systems. Navigation is reliant on global positioning systems (GPS) which are supported by a satellite network. However, satellites and radio signals are subject to interference from sunspots. Sunspots happen on regular cycles at varying strengths but their occurrence can’t be exactly predicted. The likelihood of a severe solar event is roughly twelve percent per decade; consequently, solar events are likely to impair navigation. Results will show the probability of each event and its impact on autonomous vehicle navigation. In the worst case scenario, satellites could even be permanently damaged by severe sunspots. As autonomous vehicles become a more significant portion of the economy, it is necessary that they have resilience to operate in extreme conditions. Alternative navigation procedures are proposed to enhance the resiliency of autonomous vehicles. Artificial intelligence related to place identification with relative geographic navigation and storage of most common routes is an option.
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3-D HORN

John Deere Technology Center-Shubham Jaiswal, Shrutika Upase
  • Technical Paper
  • 2020-01-1375
To be published on 2020-04-14 by SAE International in United States
3-D HORN is a vehicle to vehicle communication based technology which helps in reducing the noise pollution, which occurs, due to honking of automobile horns by letting only the drivers of the automobile to hear the horns and not the whole environment around him. To achieve this, a number of relatively small horn speakers are placed inside the car. These speakers are controlled by drivers of other cars. In this way honking will be heard only by the drivers. The most unique feature of this technology is the 3-D effect caused by the speakers which will let the driver know the location of the outside car which is honking. The 3-D effect is achieved by varying the intensity and proper allotment of sound to the positioned speakers in such a way that it will give the feel of the location of the outside car to the driver. Human detection is another important feature this technology provides. It will recognize whether the horn is honked for an automobile or for a human. In case of human…
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Mobile Robot Localization Evaluations with Visual Odometry in Varying Environments using Festo-Robotino

German Jordanian University-Abdallah Abdo, Randa Ibrahim
Michigan Technological University-Nathir A. Rawashdeh
  • Technical Paper
  • 2020-01-1022
To be published on 2020-04-14 by SAE International in United States
Autonomous ground vehicles can use a variety of techniques to navigate the environment and deduce their motion and location from sensory inputs. Visual Odometry can provide a means for an autonomous vehicle to gain orientation and position information from camera images recording frames as the vehicle moves. This is especially useful when global positioning system (GPS) information is unavailable, or wheel encoder measurements are unreliable. Feature-based visual odometry algorithms extract corner points from image frames, thus detecting patterns of feature point movement over time. From this information, it is possible to estimate the camera, i.e. the vehicle’s motion. Visual odometry has its own set of challenges, such as detecting an insufficient number of points, poor camera setup, and fast passing objects interrupting the scene. This paper investigates the effects of various disturbances on visual odometry. Moreover, it discusses the outcomes of several experiments performed utilizing the Festo-Robotino robotic platform. The experiments are designed to evaluate how changing the system’s setup will affect the overall quality and performance of an autonomous driving system. Environmental effects such…
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Road Curvature Decomposition for Autonomous Guidance

University of Nebraska-Lincoln-Ricardo Jacome, Cody Stolle, Michael Sweigard
  • Technical Paper
  • 2020-01-1024
To be published on 2020-04-14 by SAE International in United States
Vehicle autonomy is critically dependent on an accurate identification and mathematical representation of road and lane geometries. Many road lane identification systems are ad hoc (e.g., machine vision and lane keeping systems) or rely on polynomial approximations of road data and GPS positioning. A novel system is proposed in which geodetic road data is parsed along road directions and digitally stored in a road data matrix. Using mapping algorithms, the road data is converted to a smooth, differentiable path which connects critical road coordinates with curvature vectors and changes to road tangent angles. Different road data sources such as GPS or geographical scans were evaluated with this method and compared to current road design standards as per the American Association of State Highway and Transportation Officials. This approach takes advantage of standard roadway design practices, which rely on speed limit, superelevation, and empirical data for maximum lateral acceleration tolerance to determine acceptable radii of curvature for different classes of roadways. Successful implementation of this technology could accelerate autonomous vehicle and navigation research and development for…
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A Novel Asynchronous UWB Positioning System for Autonomous Trucks in An Automated Container Terminal

Tongji University-Mingyang Wang, Aiguo Zhou, Xinbo Chen, Yong Shen, Zhenyu Li
  • Technical Paper
  • 2020-01-1026
To be published on 2020-04-14 by SAE International in United States
As a key technology to autonomous vehicles, high precise positioning is essential for automated container terminals to implement intelligent dispatching and to improve container transport efficiency. In view of the unstable performance of global positioning system (GPS) in some circumstances, an ultra wide band (UWB) positioning system is developed for autonomous trucks in an automated container terminal. In this paper, an asynchronous structure is adopted in the system and a three-dimension (3D) localization method is proposed. Other than a traditional UWB positioning system with a server, in this asynchronous system, positions are calculated in vehicle. Therefore, propagation delays from the server to vehicles are eliminated and real-time performance of the system can be significantly improved. Traditional 3D localization methods based on TDOA are mostly invalid with anchors in the same plane. However, in order to guarantee anchors and tags in line of sight (LOS), anchors have to be installed in a vertical plane under the tyre cranes. Coping with this problem, an improved method is presented, which overcomes the matrix singularity. Three hyperboloids can be…
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LiDAR Based Classification Optimization of Localization Policies of Autonomous Vehicles

National Research Council Canada-Ismail Hamieh, Ryan Myers, Taufiq Rahman
  • Technical Paper
  • 2020-01-1028
To be published on 2020-04-14 by SAE International in United States
People through many years of experience, have developed a great intuitive sense for navigation and spatial awareness. With this intuition people are able to apply a nearly rules based approach to their driving. With a transition to autonomous driving, these intuitive skills need to be taught to the system which makes perception is the most fundamental and critical task. One of the major challenges for autonomous vehicles is accurately knowing the position of the vehicle relative to the world frame. Currently, this is achieved by utilizing expensive sensors such as a differential GPS which provides centimeter accuracy, or by using computationally taxing algorithms to attempt to match live input data from LiDARs or cameras to previously recorded data or maps. Within this paper an algorithm and accompanying hardware stack is proposed to reduce the computational load on the localization of the robot relative to a prior map. The principal of the software stack is to leverage deep learning and powerful filters to perform classification of landmark objects within a scan of the LiDAR. These landmarks…
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Cooperative Estimation of Road Grade Based on Multidata Fusion for Vehicle Platoon with Optimal Energy Consumption

Jilin University-Fangwu Ma, Yu Yang, Jiawei Wang, Yang Zhao, Yucheng Shen, Guanpu Wu
The Ohio State University-Bilin Aksun Guvenc, Levent Guvenc
  • Technical Paper
  • 2020-01-0586
To be published on 2020-04-14 by SAE International in United States
The platooning of automated vehicles possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platoon can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade takes great account effectting energy consumption of vehicle, the estimation of the road grade with high accuracy is the key factor for connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough in meter-level, increasing the difficulty to precisely estimate the road grade. This paper presents a novel cooperative estimation method Extended Kalman Filter (EKF) to obtain the accurate information of slopes by multidata fusion of GPS, Inertial Measurement Unit (IMU) using vehicle platoon communication, i.e. the following vehicle fuses the data which was measured by the on-board sensors and delivered by the proceding vehicle. Considering the accurate road grade information, the fuel consumption optimazition of the…