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#79 Bumper-To-Bumper At High Speeds: A Vision-Based System For High Efficiency Vehicle Platoons In Metropolitan Areas


Principal Investigator
Srinivasa Narasimhan
Status
Completed
Start Date
Jan. 1, 2015
End Date
Dec. 31, 2015
Project Type
Research Advanced
Grant Program
MAP-21 TSET National (2013 - 2018)
Grant Cycle
2015 TSET UTC - National
Visibility
Public

Abstract

Traffic congestion is a growing problem and is estimated to increase by 50% in the next 15 years without significant  action  [2].  Vehicle  platooning is  an  exciting  concept has  been  demonstrated  on  highways showing  improvements  in  fuel  efficiency,  travel  time,  safety,  and  environmental  impact. However,  the high cost of infrastructure changes and in-vehicle technologies required does not justify about 10-20% [7] efficiency  demonstrated  to-date.  Minimizing  the  inter-vehicle  distanceand  increasing  speed  of  vehicles safely  has  the  potential  to  double  or  even  triple  the  efficiencies  on  both  highways  and  city  roads.  But, unfortunately, the existing in-vehicle, V2V or V2I sensing technologies are woefully inadequate for these purposes (too slow or bulky or poor accuracy or expensive). The  goal  of  the  proposed  research  is  to  develop  computer  vision-based  system  for  high-speed  and high-precision estimation of inter-vehicle distances. Acomprehensive study will be conducted to evaluatethe impact  of  gap  distance  on  traffic  throughputand  energy  efficiencies. Based  on  the  findings,  we  will develop  a  smart,  cost-effective  sensing  and  processing  system  that  can  work  reliably  for  very  short  gap distances  in  all  lighting  and  weather  conditions.  We  already  have  collaborations  with  Traffic  21  (UTC TSET),  NSF  funded  CPS  project  partners  in  RI  and  ECE  (Hebert,  Bagnell,  Mertz),  collaborations  with industry  (funding  from  Intel,  Ford  and  likely  GM),  and  endorsements  for  collaborations  with  Pittsburgh city and local partners like GASP and CMU CREATE Labs to study environmental effects (air quality) of traffic and industrial sources. The funding from Metro 21 will help put together a larger and longer-term effort in solving the transportation, environmentaland energy problems in America.    
Description
Background and Significance 
Traffic congestion instantly became a problem when mass vehicle production and urbanization took hold in   the   early   1900s. Despite   efforts   to   reduce   congestion   by   constructing   highways   and   urban expressways, and widening roads, congestion is still a significant source of headache in cities of all sizes [1].  Traffic  has  a  profound  negative  impact  on  productivity,  fuel  expenses,  regional  economic  health, quality  of  life,  and  the  environment.  In  2013,  traffic  cost  the  U.S.  economy  $124  billion,  which  is estimated to increase to $186 billion by 2030 [2].  Additionally, commuters lost 5.5 billion hours, wasted 2.9  billion  gallons  of  fuel,  and  created  56  billion  pounds  of  excess  CO2.  To  put  those  numbers  in perspective,  commuters  in  a  small  city  like  Pittsburgh,  PA  lost  $1  billion,  46  million  hours,  21  million gallons of fuel, and created 431 million pounds of excess carbon dioxide [1].
An  ITS  approach  to  addressing  these  problems  was  proposed  by  General  Motors  at  the  1939  New York World’s Fair [3]. GM envisioned an automated highway system where vehicles could automatically drive  on  the  road  and  follow  other  vehicles  at  safe  distances  in  vehicle  platoons.  Current  prototype deployments of vehicle platoons utilize local sensors such as cameras, LIDAR, RADAR, and GPS, DSRC for vehicle-to-vehicle communications, and sensors/computers built into the infrastructure [4, 5, 6]. Such systems are able to achieve platoons traveling at 50 miles per hour with 6 to 10 meters between vehicles. With this gap, fuel waste can be reduced by 10 -20% [7] and fatalities can be reduced by 10% [8]. 

Problem Definition
Greater  fuel  efficiency  and  safety  are  possible  with  tighter  gaps  between  vehicles.  Additionally,  small gaps  are  necessary  in  metropolitan  areas  where,  unlike  highways,  vehicles  need  to  be  densely  packed between  intersections  and  traffic  lights.    Achieving  tighter  gap  spacing  (e.g.,  1  meter),  however,  is  very challenging for control systems using typical local sensors because of their slow acquisition rates and low accuracy (Table 1).  Some of these sensors, such as GPS and RADAR, are also unreliable in cities where tall  buildings  are  present.  Cameras,  however,  have  proven  robust  in  a  variety  of  road  environments  for many  computer-assisted  transportation  systems  ranging  from  complex  systems  such  as  autonomous driving  to  simpler  advanced  driving  systems  such  as  lane  detection. But  their  accuracies  and  precisions are unreliable in unconstrained environments. Wepropose a fast vision-based platform with smart sensing and lighting for  vehicle  trackingand inter-vehicle distance estimation that has the potential to overcome the issues in traditional vision-based sensing.

Approach to the Problem
Vision-Based Platform:The requirements of the vision-based platform are fast and accurate estimation of the  speed  and  distance  of  a  vehicle  in  front—information  needed  to  automatically  follow  the  vehicle.   The  method  we  propose  relies  on  projecting  a fast  modulating pattern  or  a  set  of  patterns  such  as  bar codes  on  the  rear  of  vehicles,  which  makes  it  easier  to  identify  and  locate  vehicles  in  frontand  are  not visible to the naked eye.  Based on deviations from  the  expected parameters of the  pattern, such as size, shape, and sharpness, speed and distance can be calculated. 
The  platform  will  consist  of  a  camera,  processing  unit  (e.g.,  FPGA),  and  projector.    The  camera captures  images  of  the  road  environment.  The  processing  unit  controls  the  system  and  processes  the images  captured  from  the  camera.  The  projector  displays  the  patterns.    We  have  experience  developing similar  systems  [10,  11],  but  expect  this  platform  to  require  much  faster  performance.  Therefore,  as illustrated  in  Fig.  1,  these  components  will  be  tightly  integrated  with  custom  hardware  to  ensure  high-speed, high data throughput between components.
We will use an FPGA for system control,image processing, and distance/speed calculations to take advantage of hardware acceleration and hardware parallelism.  The  FPGA board will also interface with the  camera  and  projector  for  high-speed  data  transfer.  Communication  with  a  high-speed  projector  will permit  dynamic  adjustment  of  the  patterns  projected  if  necessary,  for  example,  if  the  vehicle  is  too contoured or textured.  The platform will also have an interface for outputting navigation data for use by other vehicle sub-systems. The FPGA platform also plays theimportant role of latency reduction -order of microseconds to send data through any kind of programmableplatforms. The short latency between the camera/projectorshould  help  to  simplify  the  computations.We  have  previously  built  a low cost high-speed binary projector  [11]  and  also  have  experience  building  sensors  that  function  in  the  presence  of ambient illumination [13].Another possibility is to install a high-speed binary display (any DMD used in HUDs  will  suffice),  in  the  back  of  the  cars  that  transmit  1024x768x1000  bits  per  second  and  with  our short latency, to visually communicate between cars over long lines at high speeds and throughput.
Based  on  previous  work  in  the  area,  we  expect  the  camera  will  need  to  be  greater  than  1,000Hz. High frame rate cameras are costly, but we expect to only utilize a small portion of the camera’s sensor. Our previous work [12] with cameras that dynamically adjust exposure times based on motion willprove beneficial. It will allow an ordinary cheapcamera to be used for our purposes. 

Comprehensive  Study:The  exact  platform  speed  needed  is  unknown  due  to  a  lack  of  studies  on  gap distance impact on traffic throughput. To study the impact of gap distance on traffic throughput, we will use  traffic  modeling  software  such  as  TRANSYT  or  TSIS.    Specifically,  we  will  use  the  study  to determine the gap distance needed to provide maximal benefit for fuel savings, improved safety, commute times, etc. This optimal gap distance will then be used to determine the speed and accuracy requirements of the hardware design.

CMU Project Team
A  team  of  experts  from  multiple  research  fields  including  imaging  and  optics,  computer  vision,  and embedded  systems  will  conduct  the  proposed  research.  The  team  consists  of  Srinivasa  Narasimhan (CMU,  Robotics  Institute),  James  Hoe  (CMU,  Electrical  and  Computer  Engineering),  Robert  Tamburo (CMU,  Robotics  Institute).  Each  member  of  the  team  has  strong  experiences  in  transportation  related applications as well.

Budget
$75,000 is requested for the duration of this award to provide support for a PhD student and purchasing simulation software and hardware required to meet the goals of the proposed research.

Future Growth
The results of our study will show that vehicle platoons in metropolitan areas is worthwhile endeavor for reducing traffic congestion, and our platform can lay the groundwork for new research and development on technology for vehicle platoons in metropolitan areas.  For example, how do traffic signals need to be controlled for  tightly  packed  vehicle  platoons?  How  would  navigation  data  from  our  fast  platform  be integrated with information from slower sensors? 
Vision-based  sensors  are  the  critical  component  in  many  advance  automotive  systems  found  in vehicles today including lane departure warning, collision avoidance, automatic cruise control, headlight dimming,  blind  spot  detection,  adaptive  illumination,  etc.    We  will  develop  algorithms  for  vehicle tracking,  but  the  FPGA  can be  programmed  and  the  hardware  can  be  modified  to  work  for  any  of  these applications. Integration of our platform for any of these applications would increase response time by a significant factor.
Timeline
Jan-Apr: Gap distance study
Jan-June: FPGA board development
May-July: Camera Interface
June-Aug: Project Interface
June-Nov: Software/Algorithm Development
Sept-Dec: Performance Assessment
Dec: Report
Strategic Description / RD&T

    
Deployment Plan
Solving or even easing traffic congestion is relevant to many agencies –private and public. Internally, at CMU, we believe this is relevant to Metro 21, Traffic 21, and Scott Institute for Energy Innovation. We already have significant collaborations with University Transportation Center (TSET) and Pittsburgh city (Debra Lam, Chief Innovation Officer) who endorsed our just-funded (1.4 million USD) NSF Medium proposal (Narasimhan is PI, Hoe is Co-PI) on fundamentally re-designing computer vision based cyber-physical systems for in-vehicle, V2V and V2I technologies. The PI Narasimhan also has significant industry collaboration with Intel, Ford and now discussions are underway with GM on adopting the Smart Headlight technology in vehicles.We plan to leverage these industry partnerships to integrate our system on a vehicle with autonomous controls over speed and steering or adaptive cruise control.
Expected Outcomes/Impacts
Upon completion of the proposed research, we expect to have 1) a prototype of the vision-based platform for  vehicle  tracking,  2)  a  completed  study  on  gap  distance  requirements  for  the  platform  and  impact  on traffic throughput, 3) performance assessments of the prototype, and 4) a technical report of the research.  

The major tasks to be completed are listed below with timeline shown in table above:
-Perform simulations for study on gap distance
-Develop FPGA board, camera to FPGA board interface, and projector to FPGA board interface
-Develop system control software and image processing algorithms
-Assess performance characteristics (latency) and accuracy of the platform (distance/speed estimates)
-Write report describing the research methodology and results obtained
Expected Outputs

    
TRID


    

Individuals Involved

Email Name Affiliation Role Position
jhoe@cmu.edu Hoe, James ECE Co-PI Faculty - Research/Systems
srinivas@cs.cmu.edu Narasimhan, Srinivasa Robotics Institute PI Faculty - Research/Systems
rtamburo@cmu.edu Tamburo, Robert Robotics Institute Co-PI Faculty - Research/Systems
mpvo@cmu.edu Vo, Minh Robotics Institute Other Student - PhD

Budget

Amount of UTC Funds Awarded
$75634.00
Total Project Budget (from all funding sources)
$76401.00

Documents

Type Name Uploaded
Final Report 79_final_report_platooning.pdf Sept. 28, 2018, 5:21 a.m.

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