Connectivity and automation provide the potential to use information about the environment and future driving to minimize energy consumption. In order to achieve this goal, the designers of control strategies need to simulate a wide range of driving situations that can interact with other vehicles and the infrastructure to account for the specific powertrain characteristics of each automated vehicle. We present here a simulation framework called RoadRunner, which aims to facilitate the design of powertrain-aware eco-driving algorithms and a better understanding of the interactions between automation and powertrain technology. RoadRunner allows users to simulate both powertrain and longitudinal dynamics within a simulated environment. The user defines the scenario to be simulated by providing a route profile, intersection control types, number of vehicles, vehicle class, powertrain technology, connectivity, and automation level. RoadRunner then builds a Simulink diagram of the scenario, including the information flows between vehicles, and between vehicles and the road. After the simulation, the user can analyze the driving, component operations, and detailed energy consumption rates for each simulated vehicle. We present a case study on heavy-duty vehicles platooning. Using RoadRunner and detailed road data from digital maps, we quantify the impact of gap setting on fuel consumption, for a real-world route.
[1]
Aymeric Rousseau,et al.
A Modeling Framework for Connectivity and Automation Co-simulation
,
2018
.
[2]
J. Karl Hedrick,et al.
PRACTICAL STRING STABILITY FOR LONGITUDINAL CONTROL OF AUTOMATED VEHICLES
,
2004
.
[3]
Helbing,et al.
Congested traffic states in empirical observations and microscopic simulations
,
2000,
Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[4]
Ali Ghaffari,et al.
A historical review on lateral and longitudinal control of autonomous vehicle motions
,
2010,
2010 International Conference on Mechanical and Electrical Technology.
[5]
Xiao-Yun Lu,et al.
Influences on Energy Savings of Heavy Trucks Using Cooperative Adaptive Cruise Control
,
2018
.
[6]
Steven E Shladover,et al.
Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data
,
2014
.
[7]
Jason M. Ortega,et al.
Experimental Investigation of the Aerodynamic Benefits of Truck Platooning
,
2018
.
[8]
Xiao-Yun Lu,et al.
Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives
,
2015
.
[9]
Rajesh Rajamani,et al.
Vehicle dynamics and control
,
2005
.
[10]
Petros A. Ioannou,et al.
Autonomous intelligent cruise control
,
1993
.