Game-theoretic modeling of the steering interaction between a human driver and a vehicle collision avoidance controller

Development of vehicle active steering collision avoidance systems calls for mathematical models capable of predicting a human driver’s response so as to reduce the cost involved in field tests whilst accelerate product development. This article provides a discussion on the paradigms that may be used for modelling a driver’s steering interaction with vehicle collision avoidance control in path-following scenarios. Four paradigms, namely decentralized, noncooperative Nash, noncooperative Stackelberg and cooperative Pareto are established. The decentralized paradigm, developed based on optimal control theory, represents a driver’s interaction with the collision avoidance controllers that disregard driver steering control. The noncooperative Nash and Stackelberg paradigms are used for predicting a driver’s steering behaviour in response to the collision avoidance control that actively compensates for driver steering action. These two are devised based on the principles of equilibria in noncooperative game theory. The cooperative Pareto paradigm is derived from cooperative game theory to model a driver’s interaction with the collision avoidance systems that take into account the driver’s target path. The driver and the collision avoidance controllers’ optimization problems and their resulting steering strategies arise in each paradigm are delineated. Two mathematical approaches applicable to these optimization problems, namely the distributed Model Predictive Control and the Linear Quadratic dynamic optimization approaches are described in some detail. A case study illustrating a conflict in steering control between driver and vehicle collision avoidance system is performed via simulation. It was found that variation of driver path-error cost function weights results in a variety of steering behaviours which are distinct between paradigms.This is the accepted manuscript. The final version is available from IEEE at http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6951458&refinements%3D4262294079%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A7008592%29

microSlotted 1-Persistence Flooding in VANETs

Many Driver Support Systems in future vehicles will rely on wireless communication. This wireless communication can be divided into two categories: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V is often used for vehicles to exchange information of a local nature, e.g. co-operative following or collision avoidance. V2I can be used as ’smart road signs’, access to back-end networks (e.g. Internet) or as simple repeaters. The term VANET is key to V2V and V2I communication: Vehicular Ad hoc Network. A Driver Support System described in [1] presents an interesting problem: a vehicle should be aware of the state of traffic on a road, up to several kilometers ahead. A system called the TraffiFilter has been proposed in [2] to provide this information

Neural Network based Controller for High Speed Vehicle following Predetermined Path

The actual integration of automated control systems in vehicles such as Anti-lock Braking Systems (ABS) or Traction Control System (TCS) has proved to increase road safety and improve driver's comfort. Since most of the accidents are attributed to the fault of the driver, automated control systems in vehicle safety technology may dramatically better road safety by improving driver's performance. This thesis presents an enhanced and improved autonomous intelligent cruise control systems with obstacle collision avoidance integrated with path following/lane keeping. Obstacle collision avoidance is the ability to avoid obstaclesthat are in the vehicle's path, without causing damage to the obstacle or vehicle. Path following/lane keeping is the ability to follow the vehicle's path and keeping in its lane, as accurately as possible. The idea is to have a vehicle that drives by itself and avoids obstacles in the real world. Every instant, the vehicle decides by itself how to modify its direction according to its environment. This thesis demonstrates Gaussian functions and multi-objective cost function employed alongside with the Neural Network and optimal preview controller for control of the position of the vehicle to move while avoiding collision with obstacles. Each obstacle is represented independent of the others as a bell-shaped hump by the Gaussian functions which serve as an obstacle recognition system. Multi-objective cost function is formed for the planning strategy to generate, evaluate and select plans so that the vehicle can select which direction to move. Neural Network and optimal preview steering control are utilized to control a full linear steering model of a vehicle so as to increase path following accuracy. Optimal preview control is capable to portray the driver's vision of the path and process the knowledge while Neural Network controller has the ability to 'learn' from past errors and adjust the network to obtain specific target output. In this thesis, a MATLAB simulation environment was created to simulate the ability of a vehicle to avoid obstacles that are in the vehicle's path. Simulated obstacle avoidance has confirmed the capability of a vehicle to precisely avoid collision with obstacles while traveling on high speed along its predetermined path. i

Assessment of Collision Avoidance Strategies for an Underwater Transportation System

Transportation using multiple autonomous vehicles with detection avoidance capability is useful for military applications. It is important for such systems to avoid collisions with underwater obstacles in an effective way, while keeping track of the target location. In this paper, sensor-based and path-planning methods of external collision avoidance were investigated for an underwater transportation system. In particular, sensor-based wall-following and hard-switching collision avoidance strategies and an offline RRT* path-planning method was implemented on the simulation model of the transportation system of four Hovering Autonomous Underwater Vehicles (HAUVs). Time-domain motion simulations were performed with each method and their ability to avoid obstacles was compared. The hard-switching method resulted in high yaw moments which caused the vehicle to travel towards the goal by a longer distance. Conversely, in the wall-following method, the yaw moment was kept to zero. Moreover, the wall-following method was found to be better than the hard-switching method in terms of time and power efficiency. The comparison between the offline RRT* path-planning and wall-following methods showed that the fuel efficiency of the former is higher whilst its time efficiency is poorer. The major drawback of RRT* is that it can only avoid the previously known obstacles. In future, offline RRT* and wall following can be blended for a better solution. The outcome of this paper provides guidance for the selection of the most appropriate method for collision avoidance for an underwater transportation system

Neural Network based Controller for High Speed Vehicle following Predetermined Path

The actual integration of automated control systems in vehicles such as Anti-lock Braking Systems (ABS) or Traction Control System (TCS) has proved to increase road safety and improve driver's comfort. Since most of the accidents are attributed to the fault of the driver, automated control systems in vehicle safety technology may dramatically better road safety by improving driver's performance. This thesis presents an enhanced and improved autonomous intelligent cruise control systems with obstacle collision avoidance integrated with path following/lane keeping. Obstacle collision avoidance is the ability to avoid obstaclesthat are in the vehicle's path, without causing damage to the obstacle or vehicle. Path following/lane keeping is the ability to follow the vehicle's path and keeping in its lane, as accurately as possible. The idea is to have a vehicle that drives by itself and avoids obstacles in the real world. Every instant, the vehicle decides by itself how to modify its direction according to its environment. This thesis demonstrates Gaussian functions and multi-objective cost function employed alongside with the Neural Network and optimal preview controller for control of the position of the vehicle to move while avoiding collision with obstacles. Each obstacle is represented independent of the others as a bell-shaped hump by the Gaussian functions which serve as an obstacle recognition system. Multi-objective cost function is formed for the planning strategy to generate, evaluate and select plans so that the vehicle can select which direction to move. Neural Network and optimal preview steering control are utilized to control a full linear steering model of a vehicle so as to increase path following accuracy. Optimal preview control is capable to portray the driver's vision of the path and process the knowledge while Neural Network controller has the ability to 'learn' from past errors and adjust the network to obtain specific target output. In this thesis, a MATLAB simulation environment was created to simulate the ability of a vehicle to avoid obstacles that are in the vehicle's path. Simulated obstacle avoidance has confirmed the capability of a vehicle to precisely avoid collision with obstacles while traveling on high speed along its predetermined path. i

Modified hamiltonian algorithm for optimal lane change with application to collision avoidance

This paper deals with collision avoidance for road vehicles when operating at the limits of available friction. For collision avoidance, a typical control approach is to: (a) define a reference geometric path that avoids collision; (b) apply low-level control to perform path following. However, there are a number of limitations in this approach, which are addressed in the current paper. First, it is typically unknown whether a predefined reference path is feasible or over-conservative. Secondly, the control scheme is not well suited to avoiding a moving object, e.g. another vehicle. Further: incorrect choice of reference path may degrade performance, fast adaptation to friction change is not easy to implement and the associated low-level control allocation may be computationally intensive. In this paper we use the general nonlinear optimal control formulation, include some simplifying assumptions and base optimal control on the minimization of an underlying Hamiltonian function. A particle model is used to define an initial reference in the form of a desired global mass-center acceleration vector. Beyond that, yaw moment is taken into account for the purpose of enhancing the stability of the vehicle. The Hamiltonian function is adapted as a linear function of tyre forces and can be minimized locally for individual wheels; this significantly reduces computational workload compared to the conventional approach of forcemoment allocation. Several combinations of actuators are studied to show the general applicability of the control algorithm based on a linear Hamiltonian function. The method has the potential to be used in future vehicle control systems across a wide range of safety applications and hence improve overall vehicle agility and improve safety

Development of rear-end collision avoidance in automobiles

The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor

Synopsis of Soft Computing Techniques used in Quadrotor UAV Modelling and Control

The aim of this article is to give an introduction to quadrotor systems with an overview of soft computing techniques used in quadrotor unmanned aerial vehicle (UAV) control, modelling, object following and collision avoidance. The quadrotor system basics, its structure and dynamic model definitions are recapitulated. Further on synopsis is given of previously proposed methods, results evaluated and conclusions drown by authors of referenced publications. The result of this article is a summary of multiple papers on fuzzy logic techniques used in position and altitude control systems for UAVs. Also an overview of fuzzy system based visual servoing for object tracking and collision avoidance is given together with a briefing of quadrotor UAV control techniques efficiency study. Conclusion is that though soft computing methods are widely used with good results, there is still place for much research to be done on find more efficient soft computing tools for simple modelling, robust dynamic control and fast collision avoidance in quadrotor UAV control

Estimation of Driver Inattention to Forward Objects Using Facial Direction with Application to Forward Collision Avoidance Systems

In forward collision avoidance systems, warnings may be provided more effectively if the underlying timing is set earlier than normal when the driver’s attention is not in the forward direction of the vehicle. In this regard, we determined the following driver characteristcs: (1) the amount of horizontal facial rotation needed to keep track of a moving object in the driver’s field of view increases significantly when the horizontal viewing angle of that target object exceeds 20 degrees, (2) when the driver’s face is oriented in the forward direction, the horizontal angle of facial rotation falls within 15 degrees, and (3) the reaction time to warning lengthens in accordance with the increase in the horizontal viewing angle. In the context of forward collision warning systems, we have determined the difference in the distribution of the driver’s horizontal facial rotation angle, for cases when the driver’ attention was and was not directed to objects in the forward direction of the vehicle. Furthermore, we have measured the reaction time to warning when the driver’s face was not directed forward. Last, our findings were successfully applied to issue the onset timing of a forward collision warning system