EMBARC: error model based adaptive rate control for vehicle-to-vehicle communications

  • Authors:

  • Gaurav Bansal;Hongsheng Lu;John B. Kenney;Christian Poellabauer

  • Affiliations:

  • Toyota InfoTechnology Center, USA, Mountain View, CA, USA;University of Notre Dame, South Bend, IN, USA;Toyota InfoTechnology Center, USA, Mountain View, CA, USA;University of Notre Dame, South Bend, IN, USA

  • Venue:
  • Proceeding of the tenth ACM international workshop on Vehicular inter-networking, systems, and applications
  • Year:
  • 2013

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Abstract

Channel congestion is one of the major challenges for deployment of collision avoidance systems based on DSRC (Dedicated Short Range Communication) in large scale networks. If vehicles do not adapt to congestion conditions, DSRC transmissions could encounter extensive packet losses in areas of high vehicle density, leading to degradation in the performance of safety applications. In this paper, we propose a novel congestion control algorithm called Error Model Based Adaptive Rate Control (EMBARC) which adapts a vehicle's transmission rate as a function of channel load and vehicular dynamics. In particular, we extend Linear Integrated Message Rate Control (LIMERIC) algorithm's message rate adaptation with the capability to preemptively schedule messages based on the vehicle's movement. This leads to more transmission opportunities for vehicles with higher dynamics. The determination of a preemptive scheduling event is based on a novel suspected tracking error technique. Since LIMERIC maintains the channel load around a specific value, vehicles moving less dynamically will adapt to slightly reduced transmission rates in EMBARC. The extra transmit opportunities for highly dynamic vehicles reduce incidences of large tracking error compared to a pure LIMERIC approach. At the same time, EMBARC's use of adaptive rate control provides tracking error advantages over systems that transmit largely independent of channel load. We use simulations of a road with a winding segment to compare EMBARC with algorithms that do not take both channel load and vehicle dynamics into account. The results show that EMBARC has the best tracking accuracy among these algorithms over a wide range of node densities.