Speaker: Joshua A. Auld

Computational Science Leader, Argonne National Laboratory, USA

Joshua Auld is a Computational Transportation Engineer in Argonne’s Transportation Research Systems Modeling and Control Group, in the Energy Systems division. He completed his Masters degree in May 2007 and his Doctorate in August 2011, in the Civil and Materials Engineering Department at the University of Illinois at Chicago with a concentration in transportation. He also completed a Post-Doctoral Appointment with the University of Illinois at Chicago and Argonne’s Transportation Research and Analysis Computing Center in December 2014. Dr. Auld has experience in a variety of areas in transportation, with a primary focus on dynamic activity-based travel demand microsimulation models and the interactions between travel demand and intelligent transportation systems operations.

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Speaker: Dean D. Deter

Principal Engineer and Lab Space Manger, Oak Ridge National Laboratory, USA

Mr. Deter is the principal investigator (PI) for the Vehicle Systems Integration (VSI) and Connected and Automated Vehicle Environment (CAVE) Laboratories at ORNL. Similar to these laboratories, Mr. Deter specializes in vehicle and powertrain research and development utilizing advanced hardware-in-the-loop (HIL) practices and methodologies. Mr. Deter is also the PI for a majority of ORNL projects that focus on vehicle and powertrain HIL as well as virtual vehicle environments. Most recently Dean has created and lead the concept development for the Virtual-Physical Proving Ground at ORNL. This concept enables research and development of connected and automated vehicles in environments ranging from purely simulation to full vehicle-in-the-loop. Dean has extensive experience in vehicle/component modeling and simulation, HIL testing methods, connected and automated vehicles, virtual vehicle environments, embedded controls, and sensor data emulation.

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Speaker: Shuo Feng

Research Fellow, University of Michigan, USA

Dr. Shuo Feng received the bachelor's and Ph.D. degrees from the Department of Automation, Tsinghua University, China, in 2014 and 2019, respectively. He was also a visiting Ph.D. student in Civil and Environmental Engineering with the University of Michigan, Ann Arbor, MI, USA, from 2017 to 2019, where he is currently a research fellow. His current research interests include testing, evaluation, and optimization of connected and automated vehicles.

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Speaker: David Florence

Assistant Research Engineer, Texas A&M Transportation Institute, USA

Mr. Florence is an Assistant Research Engineer at the Texas A&M Transportation Institute. His expertise includes traffic signal systems and control, microsimulation, macrosimulation, Intelligent Traffic Systems (ITS), freeway operations, weather responsive traffic management, and traveler information systems. Mr. Florence specializes in adaptation of vehicle behavior in microsimulation for representation of connected and autonomous vehicle systems.

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Speaker: Guodong Rong

Principal Engineer, LG Silicon Valley Lab, USA

Dr. Rong is currently a principal engineer at LG Silicon Valley Lab working on simulator for autonomous vehicles. Before joining LG, he was a principal engineer at Baidu USA, a principal architect of VR/AR at Huawei Technologies, a senior software engineer at Google in YouTube VR team, a staff research engineer at Samsung Research America - Silicon Valley (SRA-SV), a senior system software engineer at NVIDIA, and a postdoctoral researcher at Department of Computer Science, University of Texas at Dallas. Dr. Rong received the Ph.D degree from School of Computing, National University of Singapore, and the M.Eng. and B.Eng. degrees from School of Computer Science and Technology, Shandong University.

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Speaker: Seyhan Uçar

Principal Researcher, Toyota Motor North America R&D - InfoTech Labs, USA

Dr. Uçar is currently working as a Principal Researcher in Intelligent Mobility Systems at InfoTech Labs, Toyota Motor North America USA. He received his B.Sc. degree in Computer Engineering from İzmir Institute of Technology in 2011. He received his M.Sc. and a Ph.D. degree in Computer Science and Engineering from Koç University in 2013 and 2017, respectively. Throughout his M.Sc. studies, he worked on developing multi-hop clustering algorithms and Long-Term Evaluation (LTE) based heterogeneous architectures for vehicular ad hoc networks. During his Ph.D., he focuses on Visible Light Communication (VLC) and automated car following (or platooning) where a group of vehicles travels within close proximity through communication. In his work, the joint usage of IEEE 802.11p and VLC is investigated to achieve secure and efficient architecture for platoon management and communication. He is now working on intelligent transportation systems and applications and analyzing the impact of connected vehicles on transportation safety and management.

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Speaker: Pin Wang

Researcher, University of California, Berkeley, USA

Dr. Wang is a researcher at California PATH, UC Berkeley. She is mainly working on deep learning based automated driving projects under Berkeley DeepDrive Consortium, and vehicle platform developments. Prior to joining PATH, Dr. Wang did research on Cooperative Collision Warning System based on V2X Technologies, Big Data Analysis on Vehicle Driving Patterns, Simulation Assessment of Advanced Vehicular Technologies, Crash Data Analysis, and Road Safety Evaluation. Dr. Wang received her Ph.D. degree in Transportation Engineering from Tongji University, China. She also worked as an intern at BMW Technology Office for half a year, and a postdoctoral researcher at PATH for a year and a half.

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Speaker: Hao Yang

Assistant Professor, McMaster University, Canada

Dr. Yang is as an Assistant Professor focusing in Transportation Engineering, in the Department of Civil Engineering at McMaster University. He completed his M.S. and Ph.D. at the University of California, Irvine. He was also awarded a second Master’s degree in Statistics from the University of California, Irvine. He completed his B.S. at the University of Science and Technology of China. Dr. Yang’s research focuses on the design and evaluation of connected and autonomous vehicle implementations to improve vehicle mobility and energy efficiency for the rapid development of smart cities. His major research interests include modeling and managing the behaviors of connected and autonomous vehicles to mitigate road congestions, to reduce vehicle energy consumption and emissions, and to improve the performance of road transportation services.

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Speaker: Mingyuan Yang

Ph.D. Student, University of California, Berkeley, USA

Mr. Yang is currently a Ph.D. student in Transportation Engineering at UC Berkeley and a graduate student researcher at California PATH, advised by Dr. Xiao-Yun Lu. His research mainly focus on autonomous vehicles, freeway management strategies, and fuel consumption and emission modeling.

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Speaker: Xuanpeng Zhao

Ph.D. Student, University of California, Riverside, USA

Mr. Zhao is currently a Ph.D. student in Electrical and Computer Engineering at the University of California, Riverside, advised by Dr. Guoyuan Wu and Dr. Matthew Barth. His research is focused on computer vision, embedded system, autonomous vehicle, and robotics.

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Workshop Agenda

Following talks were given within a half-day workshop session on Sunday, Sep. 20, 2020.

Shuo Feng
Autonomous Vehicles Safety Assessment Simulation (ASAS) Platform Based on SUMO and CARLA

Simulation-based safety assessment is a critical step in testing and evaluation of autonomous vehicles (AVs). To assess AVs’ safety performance accurately, we developed a simulation platform based on SUMO and CARLA. CARLA provides realistic inputs to AVs’ sensors such as photorealistic images that resemble real-world renderings, while SUMO generates the background vehicles (BVs) interacting with the AVs. Different from existing SUMO-based simulation models, we developed new stochastic human driving models, which can create naturalistic behavioral patterns of human drivers. Comparing with most existing models, which are deterministic and mainly calibrated for normal driving conditions, the new models can resemble safety-critical scenarios, which are critical for safety assessment of AVs.

Seyhan Uçar
Cooperative Anomalous Driving Behavior Detection and Management

Traditional traffic law enforcement and control measures, such as police force efforts and insurance repercussions, are relatively effective at addressing serious anomalous driving behavior, i.e., through fines, penalties and in worst cases, criminal charges. However, small scale anomalous driving behavior who are engaged in Aggressive/Distracted/Reckless (ADR) driving is more difficult for the traditional enforcement infrastructure to detect, much less address. Detection of such ADR driving behavior is important, otherwise, it may jeopardize the safety of other vehicles as well as the efficiency of the transportation system. In this talk, I will introduce the cooperative anomalous driving behavior detection and management system. The system leverages vehicles’ onboard resources to determine a measure of the effect that the ADR behavior has had on other vehicles and generates control instruction(s) to mitigate the negative effect of abnormal drivers. A hierarchical edge computing architecture is designed to enable cooperative anomalous driving behavior detection and management system at the city-scale and its benefit is shown through large scale simulations performed in AIMSUN.

Mingyuan Yang
Quantifying the Environmental Benefits of Capacity Enhancing Traffic Management Strategies using AIMSUN Simulation

The future of transportation with connected and automated vehicles presents challenges and opportunities in traffic management. In the near future, vehicles will be equipped with Cooperative Adaptive Cruise Control (CACC) to allow them travel safely with short headway at higher speeds, hence achieving higher capacity, alleviating congestion, and improving fuel economy. However, similar issues such as bottlenecks caused by frequent merges from freeway entrances will diminish the freeway capacity and the fuel economy benefit of connected and automated vehicles. Traffic management strategies such as freeway ramp metering and variable speed advisory have been commonly used to enhance freeway capacity and reduce delay at bottlenecks near merging on-ramps. This study explored the potential benefit of implementing ramp metering and variable speed advisory on freeways with varying market penetrations of CACC vehicles using a case study of a 13-mile freeway corridor in Sacramento, California. Simulation analysis of detailed vehicle trajectory data that precisely capture the stop-and-go waves associated with freeway merge bottlenecks has demonstrated that ramp metering and variable speed advisory can improve fuel economy by as much as 20%, and the improvement is especially significant at higher market penetrations of CACC.

David Florence
Traffic Optimization for Signalized Corridors (TOSCo) Development and Evaluation with VISSIM

The Traffic Optimization for Signalized Corridors (TOSCo) system is a vehicle-to-infrastructure connected vehicle application that uses level one autonomy to adjust a vehicle’s speed on the approach of a signalized intersection. The system uses information from the infrastructure’s DSRC broadcasts to plan a speed trajectory that allows it to either pass through the intersection without stopping or stop in a smooth, coordinated fashion to reduce the amount of time stopped at the intersection. This presentation will explain the simulation setup for developing and testing the TOSCo vehicle algorithm as well as the simulations for evaluating TOSCo performance for a corridor.

Guodong Rong
LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving

Testing autonomous driving algorithms on real autonomous vehicles is extremely costly and many researchers and developers in the field cannot afford a real car and the corresponding sensors. Although several free and open-source autonomous driving stacks, such as Autoware and Apollo are available, choices of open-source simulators to use with them are limited. In this paper, we introduce the LGSVL Simulator which is a high fidelity simulator for autonomous driving. The simulator engine provides end-to-end, full-stack simulation which is ready to be hooked up to Autoware and Apollo. In addition, simulator tools are provided with the core simulation engine which allow users to easily customize sensors, create new types of controllable objects, replace some modules in the core simulator, and create digital twins of particular environments.

Dean Deter
Connected and Automated Vehicles: Major Shifts in Vehicle R&D Methods

Rapid advancement in vehicle computing technology, connectivity, controls, and autonomous operation of advanced vehicles has increased the difficulty of testing and modeling systems that control vehicles and traffic. Much of the challenge stems from the complexity of the new system-of-systems approach required to manage connected and autonomous vehicles and vehicles equipped with advanced driver-assistance systems as they interact with other vehicles, surrounding environments, and larger traffic networks. This talk will discuss some of the simulation and hardware-in-the-loop approaches ORNL and partners are using to develop and combine new and old methods to conduct research across these areas.

Xuanpeng Zhao
Modeling and Evaluation of Autonomous Vehicles in Mixed Traffic using an Integrated SUMO-Unity Platform

With the increasing penetration rate of autonomous vehicles, the research need related to mixed traffic is increasing. To provide an easier way to test autonomous algorithms in a mixed traffic scenario, we develop an integrated SUMO-Unity platform. The platform creates user-controlled and NPC vehicles in Unity based on the traffic flow generated from SUMO. The traffic flow recreated in Unity can react to the user-controlled vehicles based on the basic car-following model and lane-change model. The user-controlled vehicles therefore can be put into a virtual transportation network by applying real-time traffic simulation.

Ping Wang
Learning Adaptable Policy via Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks

Learning from demonstrations has gained popularity in learning policies directly from expert behaviors for decision-making and control tasks. Some state-of-the-art methods include Imitation Learning (IL), Inverse Reinforcement Learning (IRL), Generative Adversarial Imitation Learning (GAIL), Adversarial Inverse Reinforcement Learning (AIRL). It does not require a reward function that is hard to be manually designed especially for complex tasks, but it generally needs abundant of demonstrations to gain the ability of mimicking expert behaviors. Furthermore, the learned behavior usually works only in that specific task environment and fails to generalize to new tasks with different distributions. In reality, it is usually the case that we continuously enrich the data set by collecting new data from new tasks or environments. For example, to learn an automated lane-change behavior, we may train our vehicle agent with thousands or even millions of labeled driving demonstrations from different cities or countries, but these demonstrations may not cover all the possible situations and we may still have new data obtained from other cities or countries that are not originally included in our training data set. In this situation, it is labor intensive and cost expensive to keep labeling all the newly acquired data and retrain the model from scratch again. Therefore, it is essential to have a model that can make good use of the knowledge learned from existing tasks and generalize quickly to new tasks with limited or even unlabeled data samples. Meta-learning is an approach to adapt learned models to novel settings by exploiting the inherent35learning similarities across a distribution of tasks. In this work, we combine Adversarial Inverse Reinforcement Learning and Meta-learning to learn the model initialization that can be quickly fine-tuned and adapted to new situations with limited data. We then apply the proposed method to complex decision-making tasks in autonomous vehicles.

Hao Yang
Anomaly Behavior Management: Reducing the Impact of Anomalous Drivers with Connected Vehicles

Passenger vehicles operated by anomalous drivers, who are distracted on roads and perform errorable driving behaviors, result in increased risk of collisions to themselves and their surrounding vehicles. Eliminating the impact of anomalous drivers to the surrounding vehicles is very critical to improve driving safety. In this study, an anomaly management system is developed with the help of connected vehicles to solve the problem. An errorable car-following model is applied to model the dynamics of anomalous vehicles and to analyze their impacts to other vehicles. The system utilizes connected vehicles to monitor the errorable behaviors of the anomaly drivers and estimates acceleration and lane changing advice for connected vehicles to avoid dangerous behaviors. In addition, a hierarchical architecture is integrated with the proposed system to reduce the risk of collision caused by anomaly vehicles in large-scale road networks. A microscopic traffic simulation is applied to evaluate the benefits of the proposed system on reducing the risk of collisions and improving mobility for both connected vehicles and whole networks. In addition, a sensitivity analysis of market penetration rates of connected vehicles and traffic demand levels will be conducted to understand the reliability of the system at different development stages of connected vehicles and traffic congestion.

Joshua A. Auld
Modeling the Impacts of Future Mobility Technologies using the POLARIS SMART Mobility Workflow

Recent advances in vehicle technologies, mobility services and transportation system management have the potential to fundamentally change the way transportation is provided and used in the near future. Technologies such as connectivity, both between vehicles and with infrastructure, and automation have the ability to allow vehicles to travel more efficiently, safely and economically, and enable new forms of mobility to emerge. However, recent studies have also shown that there is the potential for these new technologies to drastically alter the way individuals travel leading to increases in congestions, energy use, emissions and so on, in some scenarios. Different vehicle and transportation technologies interact in complex ways with the transportation system as a whole and with individual travel behavior. In order to understand these complex interactions and evaluate the potential benefits of future mobility technologies, the SMART Mobility modeling workflow was recently developed. This workflow seeks to evaluate new transportation technologies such as connectivity, automation, sharing, and electrification using multi-level simulation analysis that captures interactions between technologies and travelers. The workflow is centered around the POLARIS agent-based activity-travel demand simulation tool, and integrates through multiple other models at different levels of scale and resolution (i.e. individual vehicle simulations, connected vehicle simulations in traffic microsimulation, long-term land use simulation) to gain insights about the influence of new mobility and vehicle technologies at the system level.

Workshop Organizers

Ziran Wang, Research Scientist
Toyota Motor North America R&D - InfoTech Labs, USA
Jiaqi Ma, Associate Professor
University of California, Los Angeles, USA
Guodong Rong, Principal Engineer
LG Silicon Valley Lab, USA
Guoyuan Wu, Associate Research Engineer
University of California, Riverside, USA
Yiheng Feng, Assistant Professor
Purdue University, USA
Hao Liu, Research Engineer
University of California, Berkeley, USA
Chris Schwarz, Research Engineer
University of Iowa, USA