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A Development and Evaluation of Optimal Fingerprint Authentication Algorithm in Vehicle Use Environment

Hyundai Motor Co & KIA Motors Corp-dae sung jin, Jungduck Son, Sangwoo Jeon
  • Technical Paper
  • 2020-01-0723
To be published on 2020-04-14 by SAE International in United States
Hyundai Motor Company mass-produced the world's first fingerprint entry and start system. This paper is a study on the evaluation method to develop and verify the optimal fingerprint authentication algorithm for vehicle usage conditions. Currently, fingerprint sensors and algorithms in the IT industry have been developed for the electronic devices, and are not suitable for the harsh environment of the vehicle and the vehicle life cycle for more than 10 years. In order to optimize the fingerprint sensor and algorithm for the vehicle, this study consisted of 3way test methods. As a result, the fingerprint system could be optimized for the vehicle and the recognition rate and security could be optimized according to the sensor authentication level. Through this study, the fingerprint entry and start system achieved the recognition rate development goal (door handle sensor recognition rate: 85% or more, start button sensor recognition rate: 90% or more) and achieved security that meets European immobilizer regulation
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Alleviating the Magnetic Effects on Magnetometers using Vehicle Kinematics for Yaw Estimation for Autonomous Ground Vehicles

Michigan Technological Univ-Jeffrey Naber
Michigan Technological Univ.-Ahammad Basha Dudekula
  • Technical Paper
  • 2020-01-1025
To be published on 2020-04-14 by SAE International in United States
Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement. The present work focuses on alleviating magnetic disturbances for ground vehicles by fusing the vehicle kinematics information with IMU senor in an Extended Kalman filter (EKF) with the vehicle orientation represented using Quaternions. In addition, the error in rate measurements from gyro sensor gets accumulated as the time progress which results in drift in rate measurements and thus affecting the vehicle…
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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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Impact of Automated Lane Change Assist on Energy Consumption

Embry-Riddle Aeronautical University-Casey Troxler, Patrick Currier, Charles Reinholtz
  • Technical Paper
  • 2020-01-0082
To be published on 2020-04-14 by SAE International in United States
Automated lane change assist combined with adaptive cruise control has the potential to reduce energy consumption and improve safety. This paper models adaptive cruise control combined with automated lane change assist to investigate the energy consumption improvements that such a system may provide compared to conventional adaptive cruise control. Automatically executing a lane change may improve efficiency, for example, when following a vehicle that is slowing to make a turn. Changing lanes while maintaining speed should be more efficient than staying in the same lane as the turning vehicle and reducing speed. The differences in such scenarios are simulated in a virtual environment using a cuboid model with idealized sensors. The ego-vehicle will detect scenarios, evaluate if a lane change is feasible, and possibly perform a lane change to reduce or eliminate required speed changes. The results of the simulations compare the energy content of the resulting drive cycle as an idealized method to measure energy consumption for each cruise control strategy. The simulations consider traffic laws, such as turn signal requirements that may dictate…
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Sparse-CNN based Real-time Semantic Segmentation of Point Cloud from LiDAR

Hanyang university-Jaehyun Park, Chansoo Kim, Myoungho Sunwoo
Konkuk Univ-Kichun Jo
  • Technical Paper
  • 2020-01-0725
To be published on 2020-04-14 by SAE International in United States
A Light Detection And Ranging (LiDAR) sensor, which provides the precise location and shape information of objects near the ego-vehicle based on a point cloud, plays a core sensor in the perception of the autonomous driving. However, the point cloud cannot provide the semantic information of objects such as buildings, vehicles, and pedestrians. The absence of the semantic information can cause the degraded performances of object detection and motion prediction. For the extraction of the semantic information from the point cloud, which is called semantic segmentation, many deep learning-based methods have been researched. Since most of the deep learning-based methods have deep and bulky networks, the methods may cause insufficient real-time performance. Moreover, while the deep learning-based methods need large amounts of the labeled point cloud to train their models, the previous methods trained with a limited amount of the labeled point clouds which are obtained from a real driving environment. This shortage of the labeled point cloud may cause difficulty in the improvement of the performance. In our research, we propose the sparse-CNN based…
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Vehicle Velocity Prediction Using Artificial Neural Network and Effect of Real World Signals on Prediction Window.

CEAS Western Michigan University-Alvis Fong
Colorado State Univ-Thomas Bradley, Lowell Hanson
  • Technical Paper
  • 2020-01-0729
To be published on 2020-04-14 by SAE International in United States
Prediction of vehicle velocity is important since it can realize improvements in the fuel economy/energy efficiency, drivability and safety. Velocity prediction has been addressed in many publications. Several references considered deterministic and stochastic approaches such as Markov chain, autoregressive models, and artificial neural networks. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain inclusive datasets. Using these inclusive datasets of sensors in deep neural networks, high accuracy velocity predictions can be achieved. This research builds upon previous findings that Long Short-Term Memory (LSTM) deep neural networks provide the highest velocity prediction fidelity. We developed LSTM deep neural network which uses different groups of datasets collected in Fort Collins. Synchronous data was gathered using a test vehicle equipped with sensors to measure ego vehicle position and velocity, ADAS-derived near-neighbor relative position and velocity, and infrastructure-level transit time and signal phase and timing. Effect of different group of datasets on forward velocity prediction window of 10, 15, 20 and 30 seconds is studied. Developed algorithm is tested…
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Dynamic Object Map based architecture for robust CVS systems.

Hyundai Motor Group-SYED MAHMUD
University of Central Florida-Rodolfo Valiente, Arash Raftari, Mahdi Zaman, Yaser Pourmohammadi Fallah
  • Technical Paper
  • 2020-01-0084
To be published on 2020-04-14 by SAE International in United States
Connected and Autonomous Vehicles (CAV) rely on information obtained from sensors and communication to make decisions. In a Cooperative Vehicle Safety (CVS) system, information from remote vehicles (RV) is available at the host vehicle (HV) through the wireless network. Safety applications such as crash warning algorithms use this information to estimate the RV and HV states. However, this information is uncertain and sparse due to communication losses, limitations of communication protocols in high congestion scenarios, and perception errors caused by sensor limitations. In this paper we present a novel approach to improve the robustness of the CVS systems, by proposing an architecture that divide application and information/perception subsystems. This architecture is enabled by a Dynamic Object Map (DOM) middle layer which uses the received data from HV local sensors and integrates it with the data received through wireless communication to track RVs and create a real-time dynamic map of HV’s surrounding. The architecture is validated with simulations and in a real environment using a remote vehicle emulator (RVE), which allows the joint study of the…
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Predictive gearbox oil temperature using Machine Learning techniques

Electronics Engineering-Varaprasad Gandi
Tata Elxsi Ltd-Mithun Manalikandy, Rajesh Koduri
  • Technical Paper
  • 2020-01-0731
To be published on 2020-04-14 by SAE International in United States
Gearbox failure is the most common failure, which is being detected in vehicles, turbines and other applications. It is not possible to detect every fault manually because gearbox failure depends on various factors like gearbox oil temperature, uncertain driving patterns, engine components and other various gearbox parameters. In recent decades, a lot of research has been done in detecting gearbox failure and various methods and techniques have been proposed to predict failure and also to reduce maintenance and failure costs. To predict the behaviour of the gearbox, robust and efficient algorithms are required. In this work, an effective and accurate algorithm to predict gearbox failure after analysing various symptoms arising on gearbox oil temperature is proposed. Gearbox oil temperature variations are caused by different factors like viscosity, water saturation, dielectric constant and conductivity. Diverse machine learning models such as Support Vector Machine, Random forest and Logistic Regression algorithms from the dataset obtained from gearbox real-time observations are leveraged in this analysis. This paper aims to use sensor data for monitoring oil temperature for fault detection.…
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Heartbeat Detection Technology for Monitoring Driver's Physical Condition

Aisin Seiki Co., Ltd.-Kento Tsuchiya, Kenta Mochizuki
Keio University-Tomoaki Ohtsuki PhD, Kohei Yamamoto
  • Technical Paper
  • 2020-01-1212
To be published on 2020-04-14 by SAE International in United States
In recent years, the number of reported traffic accidents due to sudden deterioration in driver’s physical condition has been increasing, and it is expected to develop a system that prevents accidents even if physical condition suddenly changes while driving, or reduces damage through vehicle body control.For this purpose it is necessary to detect sudden changes of the driver’s physical condition, and research is being conducted widely. Among them, it is reported that some of such changes may appear in the heartbeat interval. In other words, by acquiring the driver's heartbeat interval in real time, it may be possible to detect the sudden changes, and reduce traffic accident. Even if a traffic accident has occurred, the damage can be reduced by emergency evacuation immediately after detecting sudden changes. Therefore, we focused on the technology to detect the heartbeat interval wiht 24GHz microwave doppler radar, which can acquire heartbeat non-contactly while maintaining the interior design and passenger’s privacy. Doppler radar with microwave is sensitive enough to detect heartbeat, but vibration noise is also superimposed on the sensor…
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Model predictive air path control in a diesel engine

Garrett Advancing Motion-Jaroslav Pekar, Paul Dickinson, MinSeok Ko
Hyundai Motor Group-Buomsik Shin, Yohan Chi, Minsu Kim
  • Technical Paper
  • 2020-01-0269
To be published on 2020-04-14 by SAE International in United States
A supervisory model predictive control system is developed for the air system of diesel engine. The diesel air system is complicated, composing of many components and actuators, with significant nonlinear behavior. Furthermore, the engine usually often operates in various modes, for example to activate catalyst regeneration like LNT or DPF. Model predictive control (MPC) is based on a dynamical model of the controlled system and it features predicted actuator path optimization. MPC has been previously successfully applied to the diesel air path control problem, however, most of these applications were developed for a single operating mode (often called normal operating mode) which has only one set of high-level set point values. In reality, each engine operating mode requires a different set of set point maps in order to meet the various system requirements such as, HP-EGR modes for cold start purposes, heat-up modes for after-treatment conditioning, rich operation for catalyst purging and normal modes. Air mass and its composition requirement are heavily depending on each specific mode. This large array of mode specific set points…