Two Nash-equilibrium-based steering control models for representing a driver’s interaction with vehicle automated steering

Abstract

Automated steering technology offers significant benefits to the safety and efficiency of vehicles, but desire to keep the human driver in the loop requires better understanding of the interaction between driver and vehicle. An existing noncooperative-game-theoretic framework for modelling such interaction is revisited in this paper, leading to the development of two alternative driver steering control models. Both models bears Nash-equilibrium properties but involve different assumptions about driver steering control behaviour. A simulation study is performed to demonstrate the difference between the two driver models in terms of their simulated driver steering angles and vehicle lateral motions. An experiment using a fixed-base driving simulator is conducted to measure six test subjects' steering control behaviour in response to the lanechange manoeuvres generated by an automated steering control system. The two proposed driver models are fitted to the measured driver steering angle data to investigate their capabilities for representing individual driver's steering control behaviour. Key model parameters are identified following a system identification procedure. It is found that the two driver models have equivalent capability for capturing the trend exhibited in all the six test subjects' steering angle histories, but less good at reproducing the severe overshoot or oscillation involved in two subjects' measured steering angles. It is found that the inclusion of an arm neuromuscular system model can improve the performance of the proposed driver models on representing human driver steering control behaviour.

Stochastic non‐cooperative game for safety control of shared control vehicle in uncertain environments

“…Based on the modelling method in [20,22], the vehicle model is written in the form of the equation of state as follows:…”

“…Based on the modelling method in [20, 22], the vehicle model is written in the form of the equation of state as follows: $$$$\begin{equation} {{{\dot{\bf x}}}_g} = {{{\bf A}}_g}x + {{\bf B}}_{g1} u_1 + {{\bf B}}_{g2} u_2\end{equation}$$$$where $${x_g} = {[ { \def\eqcellsep{&}\begin{array}{*{20}{c}} {v_y}&\quad{{v_x}}&\quad \phi &\quad \omega &\quad Y&\quad X \end{array} } ]^{\mathop{\rm T}\nolimits} }$$, $${u_1} = [ { \def\eqcellsep{&}\begin{array}{*{20}{c}} {{\delta _H}}&\quad{{a_H}} \end{array} } ]^T $$, $${u_2} = {[ { \def\eqcellsep{&}\begin{array}{*{20}{c}} {{\delta _A}}&\quad{{a_A}} \end{array} } ]^T }$$, …”

“…Na et al revisit existing non-cooperative game-theoretic frameworks and develop two novel driver steering control models. The differences between the two models are illustrated through simulation analysis [22]. In [23] a game theory-based lane-changing model is built.…”

Intersection Outlet Saturation Flow Rate (OSFR) Considering Game Behavior Within Intersection

“…Applying game theory to the microscopic traffic flow analysis is not new [7][8][9][10][11]21]. Some researchers use game theory to analyze how drivers make decisions when changing lanes [7].…”

Human-centered shared steering control system design for obstacle avoidance scenarios

“…The rule-based authority allocation method is designed based on experience or subjective intent, such as a linear function, 4–6 exponential function, 7,8 U-shaped function, 9 and fuzzy decision-making methods. 10–12 Nash equilibrium-based optimization methods are to view the driver and the autonomous controller as two players with dynamic interaction and convert the authority allocation into the problem of multi-objective optimization solution, such as stochastic games, 13 nonzero-sum differential game, 14 non-cooperative game, 15,16 and cooperative game. 17 These aforementioned methods can weaken the human–machine conflict effectively.…”