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Alzu'bi, Hamzeh
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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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LiDAR-Based Urban Autonomous Platooning Simulation

FEV North America Inc.-Hamzeh Alzu'bi, Tom Tasky
  • Technical Paper
  • 2020-01-0717
To be published on 2020-04-14 by SAE International in United States
The technological advancements of Advanced Driver Assistance Systems (ADAS) sensors enable the ability to achieve autonomous vehicle platooning, increase the capacity of road lanes, and reduce traffic. This article focuses on developing urban autonomous platooning using LiDAR and GPS sensors in a simulation environment. Gazebo simulation is utilized to simulate the sensors, vehicles, and testing environment. Two vehicles are used in this study; a lead vehicle that follows a preplanned trajectory, while the remaining vehicle (follower) uses the LiDAR object detection and tracking information to follow the lead vehicle. The LiDAR object detection is handled in stages: point clouds frame transformation, filtering and down-sampling, ground segmentation, and clustering. The tracking algorithm uses the clustering information to provide position and velocity of the lead vehicle which allows for vehicles platooning. This paper covers the LIDAR object detection and tracking algorithms as well as the autonomous platooning control algorithms. The developed control algorithms were tested in a simulation environment. Test results illustrate that the follower vehicle was able to attain the autonomous platooning based on the LiDAR…
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LiDAR-Based Predictive Cruise Control

FEV North America Inc.-Hamzeh Alzu'bi, Anthony T. Jarbo, Qusay Alrousan, Tom Tasky
  • Technical Paper
  • 2020-01-0080
To be published on 2020-04-14 by SAE International in United States
Advanced Driver Assistance Systems (ADAS) enable safer driving by relying on the inputs from various sensors including Radar, Camera, and LiDAR. One of the newly emerging ADAS features is Predictive Cruise Control (PCC). PCC aims to optimize the vehicle’s speed profile and fuel efficiency. This paper presents a novel approach of using the point cloud of a LiDAR sensor to develop a PCC feature. The raw point cloud is utilized to detect objects in the surrounding environment of the vehicle, calculate grade of the road, and plan the route in drivable areas. This information is critical for the PCC to define the optimal speed profile of the vehicle while following the planned path. This paper also discusses the developed algorithms of the LiDAR data processing and PCC controller. These algorithms were tested on FEV’s Smart Vehicle Demonstrator platform. Test results show that the proposed PCC was implemented successfully, allowing the vehicle to adapt its speed based on the processed data of the LiDAR sensor.
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Autonomous Driving Development Rapid Prototyping Using ROS and Simulink

FEV North America Inc.-Hamzeh Alzu'bi, Sarika Nagaraj, Qusay Alrousan, Alanna Quail
Published 2019-04-02 by SAE International in United States
Recent years have witnessed increasing interest in Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) development, motivating the growth of new sensor technologies and control platforms. However, to keep pace with this acceleration and to evaluate system performance, a cost and time effective software development and testing framework is required. This paper presents an overview utilizing Robotic Operating System (ROS) middleware and MATLAB/Simulink® Robotics System Toolbox to achieve these goals. As an example of employing this framework for autonomous development and testing, this article utilizes the FEV Smart Vehicle Demonstrator. The demonstrator is a reconfigurable and modular platform highlighting the power and flexibility of using ROS and MATLAB/Simulink® for AD rapid prototyping. High-level autonomous path following and braking are presented as two case studies. Test results demonstrate the portability, maintainability, and reliability of the presented system.
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Cost Effective Automotive Platform for ADAS and Autonomous Development

FEV North America Inc.-Hamzeh Alzu'bi, Brian Dwyer, Sarika Nagaraj, Martin Pischinger, Alanna Quail
Published 2018-04-03 by SAE International in United States
This paper presents a cost effective development platform, named FEV-Driver, for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD). The FEV-Driver platform is an electric go-kart that was converted into an x-by-wire vehicle which represents the behavior of a full-scale electric vehicle. FEV-Driver has the advantage of being a small-scale vehicle that can be used with a significant lower safety risk compared to full-sized vehicles. The ADAS/AD algorithms for this platform were developed in both Simulink and C++ software and implemented within the Robot Operating System (ROS) middleware. Besides the description of the platform, Lane Keep Assist (LKA) and Automatic Emergency Braking (AEB) algorithms are discussed, followed by a path planning algorithm which enables the vehicle to drive autonomously after a manually controlled training lap. The modular system architecture allows for complete controller exchange or adaptation to different vehicles. The adaptation and implementation of the platform into a full-scale passenger vehicle is described in the last section of this paper. The presented platform has proven to be a low-cost scalable platform for development and verification of ADAS and…
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