Your Selections

Architecture
Show Only

Collections

File Formats

Content Types

Dates

Sectors

Topics

Authors

Publishers

Affiliations

Committees

Events

Magazine

   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Cooperative Mandatory Lane Change for Connected Vehicles on Signalized Intersection Roads

Clemson University-Zhiyuan Du, Bin Xu, Pierluigi Pisu
  • Technical Paper
  • 2020-01-0889
To be published on 2020-04-14 by SAE International in United States
This paper presents a hierarchical control architecture to coordinate a group of connected vehicles on signalized intersection roads, where the vehicles are allowed change lane to follow a prescribed path. The hierarchical control strategy consists of two levels of controllers. The higher level controller acts as a centralized controller, while the lower level controller implemented in each individual car is designed as decentralized controller. In the hierarchical control architecture, the centralized intersection controller estimates the target velocity for each approaching connected vehicle to avoid red light stop based on the signal phase and timing (SPAT) information. Each connected vehicle as a decentralized controller utilizes Model Predictive Control (MPC) to track the target velocity in a fuel efficient manner. The main objective is this paper is to consider mandatory lane changing. As in the realistic scenarios, vehicles are not necessary required to drive in single lane. More specifically, they more likely change their lanes prior to signals. Hence, the vehicle decentralized controllers are prepared to cooperate with the vehicle which has mandatory lane change request (host…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

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…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Leveraging real-world driving data sets for design and impact evaluation of energy efficient control strategies.

General Motors LLC-Bharatkumar Hegde, Steven E. Muldoon
National Renewable Energy Laboratory-Michael O'Keefe, Jeffrey Gonder
  • Technical Paper
  • 2020-01-0585
To be published on 2020-04-14 by SAE International in United States
Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are non-causal and are typically intended for evaluating emission and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, we present two methods of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Advancements of MEMristor Materials in Neuromorphic Computing for Autonomous Systems.

Wayne State University-Kyle W. Brown
  • Technical Paper
  • 2020-01-0088
To be published on 2020-04-14 by SAE International in United States
The advancements in analog electronics has spurred the development of neuromorphic computing which can replicate bio-neurological processes using artificial synapses. Artificial synapses can process information faster and more efficiently than CPUs for specialized applications like sparse coding, graph searches, and constraint-satisfaction problems. Neuromorphic systems offset CPU’s lack of processing power to solve complex tasks and computations, higher parallelism, novel neural-inspired algorithms, and optimizations. Neural-inspired algorithms such as sparse coding, simultaneous localization and mapping (SLAM), path planning, and object tracking event-based cameras are necessary in development of autonomous systems. As the industry and academia realizes the limitations posed Moore’s Law, new computing and performance by MEMristors has enabled continued process-node scaling. New technology like Intel’s inspired neuromorphic microchip demonstrates the benefits of a specialized architecture for emerging applications, including some of the computational problems hardest for the internet of things (IoT) and autonomous devices to support. As new complex computing workloads grow the need for specialized architectures designed for specific applications will be in demand. Specialized architectures using specific applications are ideal for real-world applications, from…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Vision system for detecting a small object with arbitrary shape at far range

Great Wall Motor Co., Ltd.-Shunji Miyahara, Huan Li, Kunpeng Xie, Jincheng Bai
  • Technical Paper
  • 2020-01-0094
To be published on 2020-04-14 by SAE International in United States
As one of the advanced sensing technologies of the autonomous driving, we have been working on the new vision system. It focuses on detecting the small object at far ranges. It enables to detour a vehicle by avoiding small object. This system is based on the high-resolution mono-camera with narrow FOV and the algorithms for object-detection and lane-detection. Since we have developed the system for the traffic cones, we proceed to the algorithm for an unknown object with arbitrary shape. It can detect the object and estimate its range by using the information of lane detection even though the shape of the object is unknown. Here the system architecture and how to make the detection and the ranging of unknown objects are described. Some test result are also described.
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Evaluation Methodologies of Dynamically Reconfigurable Systems in the Automotive Industry

BMW Group-Florian Oszwald, Ruben Bertelo
Karlsruhe Institute Of Technology-Juergen Becker
  • Technical Paper
  • 2020-01-1363
To be published on 2020-04-14 by SAE International in United States
The technology for self-driving cars and other highly-automated applications are becoming more and more advanced. At the same time, Electrical/Electronic (E/E) architectures are becoming more complex. Classical decentralized E/E architectures based on a large number of Electronic Control Units (ECU) represent an obstacle for the realization of new applications due to the computational power, energy consumption, weight, and the size of embedded components constraints in the automotive industry. Therefore the adoption of new embedded centralized E/E architectures represents a new opportunity to tackle these challenges. However, they also raise concerns and questions about their safety, hence, an appropriate evaluation must be performed to guarantee that safety requirements resulting from an Automotive Safety Integrity Level (ASIL) according to the standard ISO 26262 are met. In this paper, an evaluation of a dynamically reconfigurable system implemented on a centralized architecture is presented. The parameters evaluated are centered in reliability, probability of failure and possible trade-offs through the implementation of redundancy into reprogrammable devices and its performance parameters. The method used is divided into three stages. The first…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

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…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Vehicle Design Considerations Enabling High-Performance Charging

Magna Steyr Fahrzeugtechnik AG & Co. KG-Christian Josef Paar, Helmut Martin Waser, Heimo Kreimaier, Inés Cuenca-Jaen, Florian Eibler
  • Technical Paper
  • 2020-01-1440
To be published on 2020-04-14 by SAE International in United States
Customer requirements such as range anxiety are the driver for increasing the charging power of battery-electric vehicles. High-performance charging theoretically enables time targets of faster than 30 kilometers (19 miles) recharging per minute. Due to physical limitations (i.e., current limits of the components available) a charging power of greater than 200 kilowatt arises the question of the voltage level required to fulfill the power demand. One possible approach to achieve high charging power is increasing the battery voltage, i.e., increasing the voltage level from 400 V to 800 V is one simple measure. This publication discusses the main aspects of charging by incorporating all high-voltage components in the vehicle. An increase of the voltage level and charging power affects all high-voltage components, especially the thermal management of the battery needs special consideration. High-voltage vehicle architecture design considerations are discussed including thermal-management and battery-design aspects. Different charging curves from existing vehicles are compared with a generic fast charging profile which is defined using theoretical background of available cells including consideration of physical and chemical limits (e.g.,…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

An Evaluation of Future Topologies and Architectures for Highly Reliable Electrical Distribution Systems

University of Kassel-Ludwig Brabetz, Mohamed Ayeb, Janis Lehmann, Benjamin Löwer
  • Technical Paper
  • 2020-01-1296
To be published on 2020-04-14 by SAE International in United States
Within the scope of the development of autonomous vehicles, the continuous introduction of automated driving functions considerably increases the mandatory reliability requirements of the electrical power supply, and consequently of the electrical distribution system (EDS). In addition, the overall rising number of electrical functions in future vehicles leads to significantly higher electrical power demands, while strict cost, weight and packaging constraints must be upheld. Current developments focus mostly on the improvement of the conventional EDS, e.g. by adding redundancies, enhancing physical robustness, or redimensioning critical components. New approaches address predictive power management, better diagnostic capabilities, and, the subject of this paper, new topologies and architectures. Alternative topologies are derivations of the conventional tree structure, as well as ring- or linear-bus-based zonal architectures, which feature in part distributed storage devices or semiconductor switches that rearrange the power paths in case of a fault. The presented approach is a method for both the systematical description of EDS topologies and architectures, and the assessment of their reliability. It is based on a data model designed for a simple…
   This content is not included in your SAE MOBILUS subscription, or you are not logged in.

Joint Calibration of Dual LiDARs and Camera using a Circular Chessboard

Tongji University-Zhenwen Deng, Lu Xiong, Dong Yin, Fengwu Shan
  • Technical Paper
  • 2020-01-0098
To be published on 2020-04-14 by SAE International in United States
Environment perception is a crucial subsystem in autonomous vehicles. In order to build safety and efficient traffic transportation, several researches have been proposed to build accurate, robust and real-time perception systems. Camera and LiDAR are widely mounted on self-driving cars and developed with many algorithms in recent years. The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. Extrinsic parameter calibration is able to align the coordinate systems of sensors and has been drawing enormous attention. However, differ from spatial alignment of two sensors’ data, joint calibration of multi-sensors (more than three devices) should balance the degree of alignment between each one. In this paper, we assemble a test platform which is made up of dual LiDARs and monocular camera and is the same as the sensing hardware architecture of intelligent sweeper designed by our laboratory. Meanwhile, we propose the related joint calibration method using a circular chessboard. The center of circular chessboard is respectively detected in camera image to get pixel coordinates…