Projects

• CAMP – Traffic Optimizer-Signalized Corridor

  Traffic Optimization for Signalized Corridors Small Scale Test & Evaluation

  To leverage the identified AERIS Transformative Concepts and Applications as well as ensure robust gains from the real-world deployment of these applications, the research team developed a Traffic Optimization for Signalized Corridors (TOSCo) system (focusing on CACC-enabled Eco-Approach and Departure application) with detailed architectures and algorithms from both sides of vehicles and infrastructure. Specific traffic corridors have been selected and the entire system is implemented in a simulation environment to evaluate potential benefits (mainly on mobility and environment) and hazards/risks

  Lead Faculty: Dr. Guoyuan Wu, Dr. Matthew Barth, Dr. Ziran Wang

• Toyota Digital Twin

  Evaluating Connected Vehicle Applications in a Mixed Traffic Environment Using a “Digital Twin” Approach

  In this project, the research team of University of California at Riverside (UCR) follows the “Digital Twin” concept developed by the Toyota InfoTechnology Center (ITC), and develops a prototype of Cooperative Ramp Merging system using the Unity game engine, a promising agent-based modeling and simulation (ABMS) tool. In addition, the research team sets up a Vehicle-to-Cloud environment using 4G/LTE communication and performs field experiments at the ramp area near the intersection between Columbia Ave. and Iowa Ave. in Riverside, CA, to validate the effect of the proposed system.
  Two 2012 Toyota Corolla passenger cars were leased by Toyota InfoTech Center to perform Connected Vehicle (CV) application development. These two test vehicles are equipped with GNSS module, 4G/LTE communication module, and human-machine interface (HMI) to conduct field operational testing on the Vehicle-to-Cloud (V2C) based Cooperative Ramp Merging System.

  Lead Faculty: Dr. Guoyuan Wu, Xishun Liao, Xuanpeng Zhao

  Publication 2020

• Honda Connect Vehicle Projects

   V2X Connected Vehicle Early Deployment Application Analysis

  This project is funded by Honda R&D Americas, Inc., who is actively looking for innovative CV-based applications with potential of high effectiveness at low penetration levels. The key objective of this project is to identify or develop this type of CV applications, model them, and evaluate their performance in terms of safety and mobility using the state-of-the-art simulation tools. The research team worked closely with HRA engineers to identify and model a few CV applications, including Lane Speed Monitoring, Electronic Emergency Brake Light, High Speed Differential Warning, Cooperative Smart Lane Selection, Anticipatory Lane Chaning, Lane Hazard Prediction, and Traffic Jam Prevention, developed the associated Application Programming Interfaces for microscopic simulation tools (such as VISSIM and Paramics), and evaluated their network-wide effectiveness using the data from the roadway networks in both Columbus, OH and Riverside, CA.

  Lead Faculty: Dr. Guoyuan Wu, Danyang Tian, Dr. Nigel Williams, Fei Ye, Dr. Kanok Boriboonsomsin, Dr. Matthew Barth, Michael Todd

• Honda Vehicle Technology Project

  Development and Evaluation of Honda's Driving Behavior Improvement Technology 

  In this research project, the UCR team developed an assessment and evaluation approach for Honda Research and Development’s Reactive Force Pedal (RFP) technology. Honda R&D has proposed and developed the RFP technology to prevent drivers from applying excessive throttle to the accelerator; hence, saving excessive fuel consumption. The UCR team developed a test plan to evaluate the RFP technology that includes specification of test course, driver sampling procedure, test standardization protocol, data quality assurance and quality control (QA/QC) procedure, experimental design, EPA MOVES related data analysis methodology, and driver and vehicle safety protocols.

  Lead Researchers: Dr. Shams Tanvir, Dr. Matthew Barth, Dr. Nigel Williams, Dylan Brown
• Glidepath - Coorperative Automated Vehicle Driving Research

  GlidePath Prototype – A Use Case of Cooperative Automation Research Mobility Applications (CARMA) Platform

  The GlidePath prototype is one of the Cooperative Automation System (CAS) applications funded by FHWA where the equipped vehicle can utilize the signal phase and timing (SPaT) information of the upcoming traffic signal(s) via wireless communications and can be controlled (at least longitudinally) to travel through the signalized intersection or corridor with minimum energy consumption and/or pollutant emissions. In this project, the researcher team focused on the development of framework for corridor-wise connected eco-driving in traffic and the design of environmentally-friendly speed profile. The Saxton Lab supported the field implementation of the proposed trajectory planning algorithm.

  Lead Faculty: Dr. Guoyuan Wu, Dr. Matthew Barth, Dr. Peng Hao

  Publication 2017

• NEXTCAR

  Connected and Autonomous Vehicles (CAV), Plug-in Hybrid Transit Vehicles, Hybrid Powertrain Control

  UC Riverside, in partnership with Oak Ridge National Laboratory and US Hybrid, led the design, development, and testing of an innovative vehicle-powertrain eco-operation system for CNG plug-in hybrid electric buses. This system uses CAV applications like predictive approach and departure at traffic signals, efficient adaptive cruise, and optimized stopping and accelerating from stop signs and bus stops. Since stop-and-go operation wastes a large amount of energy, optimizing these maneuvers for an urban transit bus presents significant opportunities for improving energy efficiency. Using look-ahead information on traffic and road grade, the team will optimize the powertrain operation by managing combustion engine output, electric motor output and battery state of charge in this hybrid application. More info.. 

  Lead Faculty: Dr. Mattew Barth, Dr. Guoyuan Wu, Dylan Brown, Zhouqiao Zhao, Dr. Peng Hao, Michael Todd, Dr. Kanok Boriboonsomsin 

• Intelligent Routing

  As part of CE-CERT’s macroscale navigation research program, we have developed over the years a number of intelligent routing algorithms. Going beyond a simple “route-by-distance” or “route-by-time” algorithm, CE-CERT researchers were one of the first research groups to developed environmentally-friendly routing algorithms, that minimized energy use and emissions. Since the 2008 seminar paper on eco-routing, we have developed a number of other routing algorithms, including dynamic incident rerouting, freeway mobility index-based navigation, distributed routing approaches, vehicle routing to mitigate human exposure to traffic-related air pollutants, and considering the exposure to traffic-related air pollution in active travelers’ route. More info 

  Lead Faculty: Dr. Mattew Barth, Dr. Kanok Boriboonsomsin, Dr. George Scora

• NOx Routing

  Routing of On-Road Heavy-Duty Deiseal Vehicles for Minimizing NOx Emissions

  This work focuses on the development of a routing methodology and algorithms to minimize NOx emissions for on-road heavy-duty vehicles. In the US, many heavy-duty diesel vehicles employ selective catalytic reduction (SCR) technology to meet newer NOx emission standards. Typically, an SCR temperature of at least 200 °C is required before a significant level of NOx reduction is achieved. This SCR temperature condition may not be met under some modes of operation, resulting in poor NOx conversion and increased tailpipe out NOx emissions. Routing developed under this work focuses on minimizing low NOx-conversion operation. 

  Lead Faculty: Dr. George Scora, Dr. Matthew Barth, Dr. Kanok Boriboonsomsin, 

• Driving Simulator System

  Evaluating Connected Vehicle Applications in a Mixed Traffic Environment Using a "Digital Twin" Approach

  The driving simulator is developed in collaboration with Toyota InfoTech Center, aiming to provide an immersive environment to perform human factor related research in the area of Connected and Automated Vehicles. The core of the simulator is driven by the Game Engine, Unity. It is also connected with Oculus VR to enable the research on interaction between vehicles and other modes (e.g., pedestrian). 

  As a good example of Advanced Driver-Assistance Systems (ADAS), Advisory Speed Assistance (ASA) helps improve driving safety and possibly energy efficiency by showing advisory speed to the driver of an intelligent vehicle. However, driver-based speed tracking errors often emerge, due to the perception and reaction delay, as well as imperfect vehicle control, degrading the effectiveness of ASA system. In this study, we propose a learning-based approach to modeling driver behavior, aiming to predict and compensate for the speed tracking errors in real time. Subject drivers are first classified into different types according to their driving behaviors using the k-nearest neighbors (k-NN) algorithm. A nonlinear autoregressive (NAR) neural network is then adopted to predict the speed tracking errors generated by each driver. A specific traffic scenario has been created in a Unity game engine-based driving simulator platform, where ASA system provides advisory driving speed to the driver via a head-up display (HUD). A human-in-the-loop simulation study is conducted by 17 volunteer drivers, revealing a 53% reduction in the speed error variance and a 3% reduction in the energy consumption with the compensation of the speed tracking errors. The results are further validated by a field implementation with a real passenger vehicle.

  Lead Researchers: Dr. Guoyuan Wu, Dr. Zeron Wang, Dr. Matthew Barth, Dr. Kanok Boriboonsomsin, Heeson  Liao, Xiafeng Zhang
  
  Publication 2020