In a dynamic computer game in which the computer is also a decision maker, you may often find yourself competing with the game to reach your goal. Similarly in handling a "self-driving" car, automobile equipped with automated driving technology, human drivers sometimes also need to fight the car for the steering wheel in order to keep the self-driving experience safe to himself / herself and others, and ultimately get to the desired destination. Until now, the majority of studies on this driving interaction has been largely based on non-cooperate game theory, in which the driver and the computer's decisions on how to steer the car do not match.
To better understand and predict the outcomes of the steering wheel control dilemma, contrary to many previous studies, in a paper published this September in IEEE/CAA Journal of Automatica Sinica, a joint publication of the IEEE and the Chinese Association of Automation, authors Dr. Xiaoxiang Na and Dr. David J. Cole from University of Cambridge argued that using the cooperative game theory to model this type of interaction problems may be more suitable.
Using the cooperative game theory, the authors created their scenario in a "Pareto equilibrium" sense, a kind of balanced state in which neither the human driver nor the car's automated steering system is willing to change their steering behavior one-sidedly. However, in this "Pareto" state, although the human driver and the automated steering system have different ideas where the car should go for example to avoid a pedestrian, by either going straight or making a lane change, the human driver will somewhat agree with the automated steering system's intention and make decisions accordingly.
In order to find out whether being cooperative would bring more benefits to the self-driving experience, the authors also compared their modeling results with the results derived from the non-cooperative game theory.
The authors pointed out that using the cooperative strategies has "resulted in a reinforcement of the driver's steering angle control, which in turn enabled the car to return from a risky lane-change maneuver back to a safer straight-line travelling quickly."
In the future, more experiments will be needed to further examine how the cooperative strategy will influence human driver's interactions with the automated steering system. The authors hope that what they find could be built into automated steering technology so that the technology can take into account of human drivers' real-life steering experience to achieve better shared control between the humans and the self-driving cars.
Fulltext of the paper is available:
IEEE/CAA Journal of Automatica Sinica aims to publish high-quality, high-interest, far-reaching research achievements globally, and provide an international forum for the presentation of original ideas and recent results related to all aspects of automation. Researchers (including globally highly cited scholars) from institutions all over the world, such as MIT, Yale University, Stanford University, University of Cambridge, Princeton University, select to share their research with a large audience through JAS.
We are pleased to announce IEEE/CAA Journal of Automatica Sinica's latest CiteScore is 5.31, ranked among top 9% (22/232) in the category of "Control and Systems Engineering", and top 10% (27/269, 20/189) both in the categories of "Information System" and "Artificial Intelligence". JAS has been in the 1st quantile (Q1) in all three categories it belongs to.
Why publish with us: Fast and high quality peer review; Simple and effective online submission system; Widest possible global dissemination of your research; Indexed by IEEE, ESCI, EI, Scopus, Inspec. JAS papers can be found at http://ieeexplore.