Quantifying the impact of limited information and control robustness on connected automated platoons

Interaction between vehicle connectivity and automation has the potential to improve the safety, comfort, and energy efficiency of passenger road transportation. Differing approaches to connected automated vehicle following with and without limited preview information and worst-case rear-end collision robustness are presented. Given information on the preceding vehicle's current acceleration demand that may be coarse and discrete, a combined statistical and kinematic model is used to generate a prediction of future preceding vehicle motion. Model predictive control is then applied to produce a safe and smooth velocity trace for the ego vehicle, which is shown to improve energy efficiency. Simulation results compare acceleration, jerk, fuel efficiency, and road space utilization metrics of differing connected anticipative approaches with those of the intelligent driver model in 10-vehicle platoons.

[1]  Charles Schenk,et al.  Downsized Boosted Engine Benchmarking and Results , 2015 .

[2]  Chaozhe R. He,et al.  Fuel Consumption Optimization of Heavy-Duty Vehicles With Grade, Wind, and Traffic Information , 2016 .

[3]  Mike Lukuc,et al.  Vehicle-to-Vehicle Communications: Readiness of V2V Technology for Application , 2014 .

[4]  Rachel M. Malinauskas.  The Intelligent Driver Model: Analysis and application to Adaptive Cruise Control , 2014 .

[5]  Quan Hung Tran,et al.  Modelling of traffic situations at urban intersections with probabilistic non-parametric regression , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[6]  T. Berntsen,et al.  Climate forcing from the transport sectors , 2008, Proceedings of the National Academy of Sciences.

[7]  Ilya Kolmanovsky,et al.  Stochastic Fuel Efficient Optimal Control of Vehicle Speed , 2014 .

[8]  Ardalan Vahidi,et al.  Ultracapacitor power assist with preview-based energy management for reducing fuel consumption of heavy vehicles , 2016 .

[9]  Sebastian Thrun,et al.  Stanley: The robot that won the DARPA Grand Challenge , 2006, J. Field Robotics.

[10]  Jun-ichi Imura,et al.  Smart Driving of a Vehicle Using Model Predictive Control for Improving Traffic Flow , 2014, IEEE Transactions on Intelligent Transportation Systems.

[11]  Huei Peng,et al.  Strategies to minimize the fuel consumption of passenger cars during car-following scenarios , 2012 .

[12]  Huei Peng,et al.  Effect of Pulse‐and‐Glide Strategy on Traffic Flow for a Platoon of Mixed Automated and Manually Driven Vehicles , 2015, Comput. Aided Civ. Infrastructure Eng..

[13]  Eduard Zaloshnja,et al.  The Economic and Societal Impact of Motor Vehicle Crashes, 2010 (Revised) , 2015 .

[14]  Junichi Murata,et al.  Ecological Vehicle Control on Roads With Up-Down Slopes , 2011, IEEE Transactions on Intelligent Transportation Systems.