Energy Efficient Driving in Dynamic Environment: Considering Other Traffic Participants and Overtaking Possibility

This chapter studies energy efficient driving of (semi)autonomous electric vehicles operating in a dynamic environment with other traffic participants on a unidirectional, multi-lane road. This scenario is considered to be a so called hard problem, as constraints imposed are varying in time and space. Neglecting the constraints imposed from the surrounding traffic, the generation of an energy optimal speed trajectory may lead to bad results, with the risk of low driver acceptance when applied in a real driving environment. An existing approach satisfies constraints from surrounding traffic by modifying an existing unconstrained trajectory. In contrast to this, the proposed approach incorporates a leading vehicle’s motion as constraint in order to generate a new optimal speed trajectory in a global optimal sense. First simulation results show that energy optimal driving considering other vehicle participants is important. Even in simple setups significantly (8%) less energy is consumed at only 1.3% travelling time prolongation compared to the best constant speed driving strategy. Additionally, the proposed driving strategy is using 4.5% less energy and leads to 1.6% shorter travelling time compared to the existing overtaking approach. Using simulation studies, the proposed energy optimal driving strategy is analyzed in different scenarios.

[1]  Bart van Arem,et al.  Eco-routing: Comparing the fuel consumption of different routes between an origin and destination using field test speed profiles and synthetic speed profiles , 2011, 2011 IEEE Forum on Integrated and Sustainable Transportation Systems.

[2]  Jonas Sjöberg,et al.  Predictive cruise control with autonomous overtaking , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[3]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[4]  Nasser L. Azad,et al.  Ecological Adaptive Cruise Controller for Plug-In Hybrid Electric Vehicles Using Nonlinear Model Predictive Control , 2016, IEEE Transactions on Intelligent Transportation Systems.

[5]  Erik Hellström,et al.  Explicit use of road topography for model predictive cruise control in heavy trucks , 2005 .

[6]  Rochdi Trigui,et al.  Trajectory optimization for eco-driving taking into account traffic constraints , 2013 .

[7]  Harald Waschl,et al.  Comfort Oriented Robust Adaptive Cruise Control in Multi-Lane Traffic Conditions , 2016 .

[8]  Ardalan Vahidi,et al.  Reducing idling at red lights based on probabilistic prediction of traffic signal timings , 2012, 2012 American Control Conference (ACC).

[9]  Carlos Canudas de Wit,et al.  Eco-driving in urban traffic networks using traffic signal information , 2013, 52nd IEEE Conference on Decision and Control.

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

[11]  Meng Wang,et al.  Game theoretic approach for predictive lane-changing and car-following control , 2015 .

[12]  Tzila Shamir,et al.  How should an autonomous vehicle overtake a slower moving vehicle: design and analysis of an optimal trajectory , 2004, IEEE Transactions on Automatic Control.

[13]  Erik Hellström,et al.  Look-ahead Control of Heavy Vehicles , 2010 .

[14]  Chris Bingham,et al.  Impact of driving characteristics on electric vehicle energy consumption and range , 2012 .

[15]  R. Bellman The theory of dynamic programming , 1954 .

[16]  Stephen Jones,et al.  Traffic light assistant system for optimized energy consumption in an electric vehicle , 2014, 2014 International Conference on Connected Vehicles and Expo (ICCVE).

[17]  Antonio Sciarretta,et al.  Optimal Ecodriving Control: Energy-Efficient Driving of Road Vehicles as an Optimal Control Problem , 2015, IEEE Control Systems.

[18]  Bart Saerens,et al.  Optimal Control Based Eco-Driving , 2012 .

[19]  Takayoshi Yoshimura,et al.  Efficient vehicle driving on multi-lane roads using model predictive control under a connected vehicle environment , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).