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Alrousan, Qusay
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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 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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