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The Design of Safe-Reliable-Optimal Performance for Automated Driving Systems on Multiple Lanes with Merging Features

Honda Motor Co., Ltd.-Kaijiang Yu
  • Technical Paper
  • 2020-01-0122
To be published on 2020-04-14 by SAE International in United States
Safety function for automated driving systems including advanced driver assistance systems and autonomous vehicle systems is very important. Inside safety function, predictive judge sub-function should be designed with the consideration of more and more penetration of automated driving vehicles. This paper presents the design on multiple lanes with merging features based on the author's previous Patent JP2019-147944. In the author's previous work (Model Predictive Control for Hybrid Electric Vehicle Platooning Using Slope Information-Published on IEEE transactions on Intelligent Transportation Systems), a model predictive control framework was designed. Due to the difficulty to detail the sub-safety function deeply with merging features, few works are found to deal with sensor platforms focusing on rear side, and situations of merging lane side with the consideration of relative relation variations with other vehicles and road border markers. However, performance enhancement is needed assuring 100% safety-reliability-optimality and single-objectivity. Also, the detailed platform of on-board sensors including side and rear view is needed to deal with false negative operations and false positive operations. The optimal operation line model of human factors…
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Control Model of Automated Driving Systems based on SOTIF Evaluation

PATAC-Mengge Guo, Shiliang Shang, Cui Haifeng
Shanghai Jiao Tong University-Kaijiong Zhang, Weishun Deng, Xi Zhang, Fan Yu
  • Technical Paper
  • 2020-01-1214
To be published on 2020-04-14 by SAE International in United States
In partially automated and conditionally automated vehicles, part of the work of human drivers is replaced by the system, and the main source of safety risks is no longer system failures, but non-failure risks caused by insufficient system function design. The absence of unreasonable risk due to hazards resulting from functional insufficiencies of the intended functionality or by reasonably foreseeable misuse by persons, is referred to as the Safety Of The Intended Functionality (SOTIF). Drivers have the responsibility to supervise the automated driving system. When they don't agree with the operation behavior of the system, they will interfere with the instructions. However, this may lead to potential risks. In order to discover the causes of human misuse, this paper takes the trust feeling between the driver and the automated driving system as the starting point, and based on the collected data of track test, establishes the evaluation index -- confidence degree to show the trust feeling between the driver and the automated system. Confidence degree is a comprehensive interpretation of the driver's physiological and psychological…
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Real-time Motion Classification of LiDAR Point Detection for Automated Vehicles

Hanyang University-Chansoo Kim, Sungjin Cho, Myoungho Sunwoo
Konkuk University-Kichun Jo
  • Technical Paper
  • 2020-01-0703
To be published on 2020-04-14 by SAE International in United States
A Light Detection And Ranging (LiDAR) is now becoming an essential sensor for an autonomous vehicle. The LiDAR provides the surrounding environment information of the vehicle in the form of a point cloud. A decision-making system of the autonomous car is able to determine a safe and comfort maneuver by utilizing the detected LiDAR point cloud. If the movement class (dynamic or static) of detected points can be provided by LiDAR, the decision-making system is able to plan the appropriate motion of the autonomous vehicle according to the movement of the object. This paper proposes a real-time process to segment the motion states of LiDAR points. The basic principle of the classification algorithm is to classify the point-wise movement of a target point cloud through the other point clouds and sensor poses. First, a fixed-size buffer store the LiDAR point clouds and sensor poses for a constant time window. Second, motion beliefs of the target point cloud against other point clouds and sensor pose in the buffer are estimated, respectively. Each motion belief of the…
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A Human Body Model Study on Restraints for Side-Facing Occupants in Frontal Crashes of an Automated Vehicle

Joyson Safety Systems-Maika Katagiri, Sungwoo Lee
Joyson Safety Systems, NA-Jay Zhijian Zhao
  • Technical Paper
  • 2020-01-0980
To be published on 2020-04-14 by SAE International in United States
This study is to investigate kinematics and responses of side-facing seated occupants in frontal crashes of an automated minivan using Global Human Body Models Consortium (GHBMC) simplified occupant models (50th%ile male and 5th%ile female), and to develop new restraint concepts to protect the occupants. The latest GHBMC M50-OS and F05-OS models (version 2.1) were further validated with the Postmortem Human Subject (PMHS) side sled tests [Cavanaugh 1990] and the PMHS far-side sled tests [Formen 2013], with detailed correlations of the kinematics and the injury measures. Robustness and biofidelity of the GHBMC human models, especially for the pelvis and knee body regions, were further improved. Using the improved M50-OS and F05-OS models, we evaluated the body kinematics and injury measures of the side-facing seated occupants in frontal crashes at severities ranging from 15 mph to 35 mph. Three restraint conditions were studied: 1) no restraint; 2) lap belt only; 3) lap belt and conceptual inflatable device. An additional parametric study on the restraint design parameters of the #3 restraint concept was performed to “optimize” the restraint…
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Optimal Cooperative Path Planning Considering Driving Intention for Shared Control

Hunan University-Mingjun Li, Xiao-lin Song, Haotian Cao
University of Waterloo-Dongpu Cao
  • Technical Paper
  • 2020-01-0111
To be published on 2020-04-14 by SAE International in United States
Shared control is the solution that incorporates the capabilities of the human driver and the automated driving system together, and has received increasing attention in recent studies. This paper presents an optimal cooperative path planning method for shared control to address the target path conflicts during the driver-automation interaction by using the convex optimization technique based on the natural cubic spline and the driving intention recognition. The optimal path criteria (e.g. the optimal curvature, the optimal heading angle) are formulated as quadratic forms using the natural cubic spline, and the initial normal distance to the center lane of the cooperative path in the Fernet-based coordinate system is induced by considering the driver’s lane-changing intention utilized by the Support Vector Machine (SVM) method. Then, the optimal cooperative path could be obtained by the convex optimization technical. The noncooperative game theory is adopted to model the driver-automation interaction in this shared control framework, where the Nash equilibrium solution is derived by the model predictive control (MPC) approach. Finally, the proposed framework is tested with different driver’s driving…
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Concurrent Optimization of Vehicle Dynamics and Powertrain Operation Using Connectivity and Automation

University Of Delaware-A M Ishtiaque Mahbub, Andreas A. Malikopoulos
  • Technical Paper
  • 2020-01-0580
To be published on 2020-04-14 by SAE International in United States
Connected and automated vehicles (CAVs) provide the most intriguing opportunity to reduce energy consumption and travel delays. In this paper, we propose a two-level control architecture for CAVs to optimize (1) the vehicle's speed profile, aimed at minimizing stop-and-go driving, and (2) the powertrain efficiency of the vehicle for the optimal speed profile derived in (1). The proposed hierarchical control framework can be implemented onboard the vehicle in real time with minimal computational effort. We evaluate the effectiveness of the efficiency of the proposed architecture through simulation in Mcity using a 100% penetration rate of CAVs. The results show that the proposed approach yields significant benefits both in energy and travel time.
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Dyno-in-the-Loop: An Innovative Hardware-in-the-Loop Development and Testing Platform for Emerging Mobility Technologies

Oak Ridge National Laboratory-Zhiming Gao, Tim LaClair
University of California Riverside-Guoyuan Wu, Dylan Brown, Zhouqiao Zhao, Peng Hao, Michael Todd, Kanok Boriboonsomsin, Matthew Barth
  • Technical Paper
  • 2020-01-1057
To be published on 2020-04-14 by SAE International in United States
Today’s transportation is quickly transforming with the advent of shared-mobility, vehicle electrification, connected vehicle technology, and vehicle automation. These technologies will not only affect our safety and mobility, but also our energy consumption, air pollution, and climate change. As a result, it is of unprecedented importance to understand the overall system impacts, as a result of introducing these emerging technologies and concepts. However, existing modeling tools are not able to properly capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. For example, it is quite challenging to calibrate state-of-the-art microscopic traffic simulators to properly model the behavior of automated vehicles or to address potential cyber-security issues in a Connected Vehicle (CV) environment. It is even more difficult to scale up the assessment on a larger spatial scale (e.g., statewide, nationwide) or to project these impacts over a longer temporal span. To address these gaps, we have developed a Dyno-in-the-Loop (DiL) development and testing approach which integrates a test vehicle, a chassis dynamometer, and high fidelity…
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Modes of Automated Driving System Scenario Testing: Experience Report and Recommendations

AAA NCNU-Paul Wells, Atul Acharya
Silicon Valley Mobility-Sven Beiker
  • Technical Paper
  • 2020-01-1204
To be published on 2020-04-14 by SAE International in United States
With the widespread development of automated vehicles (AV), it is imperative that standardized testing methodologies be developed to ensure safety and functionality. One way to perform whole-system testing is to observe the behavior of a subject automated vehicle (SV) in many predefined scenarios. This paper presents a method for automated vehicle scenario testing and four modes for applying that method. The four modes are: closed-course testing with manually controlled actors, closed-course testing with automatically controlled actors, simulation testing, and closed-course testing with mixed reality. We discuss the advantages and disadvantages of each mode. We have excluded open-road testing from our discussion as it requires a different testing method from the one presented here. In collaboration between the Waterloo Intelligent Systems Engineering (WISE) Lab and AAA Northern California, Nevada & Utah, we executed six automated vehicle test scenarios involving pedestrians, vehicles, and road debris both at a closed course, in simulation, and in mixed reality. The SV was the University of Waterloo's automated vehicle, the "UW Moose". Drawing on both data and the experience gained from…
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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…
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A Study of Using a Reinforcement Learning Method to Improve Fuel Consumption of a Connected Vehicle with Signal Phase and Timing Data

The University of Alabama-Ashley Phan, Hwan-Sik Yoon
  • Technical Paper
  • 2020-01-0888
To be published on 2020-04-14 by SAE International in United States
Connected and automated vehicles (CAVs) promise to reshape two areas of the mobility industry: the transportation and driving experience. The connected feature of the vehicle uses communication protocols to provide awareness of the surrounding world while the automated feature uses technology to minimize driver dependency. Constituting a subset of connected technologies, vehicle-to-infrastructure (V2I) technologies provide vehicles with real-time traffic light information, or Signal Phase and Timing (SPaT) data. In this paper, the vehicle and SPaT data are combined with a reinforcement learning (RL) method as an effort to minimize the vehicle’s energy consumption. Specifically, this paper explores the implementation of the deep deterministic policy gradient (DDPG) algorithm. As an off-policy approach, DDPG utilizes the maximum Q-value for the state regardless of the previous action performed. In this research, the SPaT data collected from dedicated short-range communication (DSRC) hardware installed at 16 real traffic lights is utilized in a simulated road modeled after a road in Tuscaloosa, Alabama. The vehicle is trained using DDPG and the SPaT data to determine the optimal action to take in…