Framework for Utilization of Mobile Data Collection. Parth Bhavsar (Rowan University) Nidhal Bouaynaya (Rowan University) Jeevanjot Singh (NJDOT)

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Transcription:

Framework for Utilization of Mobile Data Collection Parth Bhavsar (Rowan University) Nidhal Bouaynaya (Rowan University) Jeevanjot Singh (NJDOT)

Agenda Parth Bhavsar Problem statement & Background Overall concept System Architecture Nidhal Bouaynaya Rowan initiative Image processing algorithm Jeevanjot Singh Benefits Challenges Future

Question: Where are we? Connected Vehicles Semi-Autonomous Vehicles Autonomous Vehicles Connected-Automated Vehicles Flying cars? Connected Vehicle Projects http://www.aeromo bil.com/ Movie: 5 th Element

Automated Vehicle- Automation Levels 5 4 Full automation High automation Automated System monitors driving environment 4 3 Full self-driving automation Limited self-driving automation 3 2 1 Conditional automation Partial automation Driver assistance Driver monitors driving environment 2 1 Combined function automation Function-specific automation 0 No automation 0 No automation SAE Level NHTSA Level 4 SAE J3016: http://www.sae.org/misc/pdfs/automated_driving.pdf NHTSA: www.nhtsa.gov/staticfiles/rulemaking/pdf/automated_vehicles_policy.pdf

Data Dissemination: Conventional - Autonomous System Notification 5 Full automation 4 Full self-driving automation 4 High automation 3 Limited self-driving automation 3 2 1 0 Conditional automation Partial automation Driver assistance No automation 2 1 0 Combined function automation Function-specific automation No automation SAE Level Driver Notification NHTSA Level SAE J3016: http://www.sae.org/misc/pdfs/automated_driving.pdf NHTSA: www.nhtsa.gov/staticfiles/rulemaking/pdf/automated_vehicles_policy.pdf 5

Challenges for DOTs Efficient transition from current transportation system to future connected-automated system Optimize existing resources Satisfy user requirements over time Continue maintaining infrastructure components Balance budget Existing Requirements Transition Future user requirements

State of the art Wyoming Department of Transportation (WYDOT) Road Condition Reporting Application for Weather Responsive Traffic Management Improve the efficiency of road condition reporting by maintenance staff. Improve the efficiency of the transportation Management Center (TMC) operations in responding to the reported road conditions. Improve the timeliness of updated traveler information. Improve the situational awareness of maintenance staff in the field regarding road weather conditions

State of the art Minnesota 80 snowplows with onboard sensors Type of material dispatched Location Route Nevada 40 vehicles in Reno-Carson city area with advanced sensors and equipment to enhance the maintenance management Michigan 60 fleet vehicles (20 snow plow trucks and 40 light- and mediumduty vehicles) Camera Image of road conditions location, time and direction Temperature, humidity dew point ABS, traction control, wheel speed

Problem Statement Develop a platform for user and location specific data collection Optimize existing resources Develop an affordable yet portable system SSP Vehicle Existing Vehicle Android UI Mobile & connected platform Prototype casing Portable Platform

Communication Platform Goal: Optimize the usage of existing vehicle fleet Collect data while performing routine tasks Develop a common communication platform to receive data from various sensors and send/disseminate data based on priority Develop portable data collection units Traffic data Pavement Data Weather Data

Communication Platform: Potential High resolution location specific data is the necessity for several connected vehicle applications. Connected Vehicle Reference Implementation Architecture (CVRIA) Type of Applications Environmental (22 applications) Mobility (36 applications) Safety (30 applications) Support (9 applications)

Communication Platform: Potential

Team NJDOT NJIT Rowan Smart Vehicle Research Group Rowan Graduate and Undergraduate students from CEE & ECE Clemson Graduate Student

Agenda Parth Bhavsar Problem statement & Background Overall concept System Architecture Nidhal Bouaynaya Pothole detection sensor Image processing algorithm Jeevanjot Singh Benefits Challenges Future

System Architecture TMC Subsystem Vehicle Subsystem Roadway Subsystem

Mobile data collection platform Communication Platform Mobile application based on the INFLO (Intelligent Network Flow Optimization) application (open source application available from USDOT/ITS JPO) Data Collection Units Pothole detection sensor Microsoft-Kinect to collect images Image processing algorithm to detect pothole Temperature and humidity sensor Infra Red Thermometer Sensor Arduino Bluetooth Transceiver

Current Status Pothole detection Vehicle Location Temperature and humidity sensor Server side user interface Android application Data collection

Prototype Evaluation Pothole detection Temperature and humidity sensor

Prototype Pavement conditions Location, Traffic & weather

Mobile data collection platform: Future recommendations Develop a comprehensive mobile data collection platform that can help NJDOT transition from current transportation system to a next generation connected & automated transportation system. Integrate other sensors as required Integrate DSRC protocols to disseminate data Develop plug and play functionality for future integration Support existing and future decision support system by providing high resolution location specific data

Agenda Parth Bhavsar Problem statement & Background Overall concept System Architecture Nidhal Bouaynaya Pothole detection sensor Image processing algorithm Jeevanjot Singh Benefits Challenges Future

System wide Benefits Location Specific HighRes Data Demand

System wide Benefits Location Specific HighRes Data Demand

Pavement condition assessment and management Current Practice Annual data collection (4600+ miles) High Speed Profiler Predict International Roughness Index (IRI) or Remaining service life. Future Significant increase in data collection frequency Utilize Safety Service Petrol (SSP) and other vehicles to collect pavement data Improve/Optimize pavement design, prioritize projects, & save $$$

Weather Responsive Traffic Management Current Practice Rich data collection units (camera, loop detectors, radar etc..) Majority of the data collection units are stationary units (i.e. road side units) Backbone for various traffic, incident and emergency management services Real-time traffic information dissemination

Future Weather Responsive Traffic Management Mobile data collection units Location specific data Connected yet affordable solution Improve overall location specific accuracy TSM&O for connected and automated vehicle fleet

System wide Benefits Location Specific HighRes Data Demand

System wide Benefits Location Specific HighRes Data Demand

Challenges

Challenges: Deployment Standards USDOT is working with public and private sector to define: Communications standards for DSRC Other media, e.g., 4G LTE and/or HD radio may be used for appropriate applications Information exchange standards: Message sets for V2X [SAE J2735] Minimum performance requirements for V2X messaging [SAE J2945.x] Certification to ensure off-the-shelf interoperability of devices

Challenges: Funding At present USDOT is promoting pilot deployment program through research funding However, in future the responsible city, county and state governments will pay for implementation and maintenance. They have to Identify resources to fund new CVT based projects Maintain existing infrastructure Bear additional burden of maintaining and upgrading the new CVT implementation Identify innovative ways to generate additional revenues

Challenges: Operations The federal, state and local governments will have to Develop standard procedures to implement, maintain and operate CVTs. Employ skilled personnel Provide training to existing employees

Challenges: Privacy & Security This hacked traffic sign on Jersey Shore route may make you stop 2016 October: DDoS attack crippled east coast web 2015: Hackers Remotely Kill a Jeep on the Highway 2016: Keen Security demonstrated that Tesla cars can be hacked into and controlled remotely. Source: https://www.truevault.com/blog/massive-iot-ddos-attack.html Video Source: 2015 Jeep Hack https://www.wired.com/video/2015/07/hackers-wireless-jeep-attackstranded-me-on-a-highway/ 2016 Tesla Hack https://www.youtube.com/watch?v=c1xyhrenchy 36

Challenges: Privacy and Security Developing a general consensus of secure but connected & automated environment Safe, Secure, & Sustainable Transportation System of the future Users of the transportation systems Transportation Professionals Cybersecurity Professionals

Questions?