Safe semi-autonomous control with enhanced driver modeling (original) (raw)

Driver models to increase the potential of automotive active safety functions

2010 18th European Signal Processing Conference, 2010

This paper describes how the potential of some automotive active safety functions depend on the used driver model. It is shown that by including a more advanced driver model, it is possible to enhance the use of the signals from different sensor systems to let the active safety function intervene earlier and smoother so that the drivers are disturbed less, and the chance to avoid an accident increases.

Drivers' Manoeuvre Prediction for Safe HRI

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018

Machines with high levels of autonomy such as robots and our growing need to interact with them creates challenges to ensure safe operation. The recent interest to create autonomous vehicles through the integration of control and decision-making systems makes such vehicles robots too. We therefore applied estimation and decision-making mechanisms currently investigated for human-robot interaction to human-vehicle interaction. In other words, we define the vehicle as an autonomous agent with which the human driver interacts, and focus on understanding the human intentions and decision-making processes. These are then integrated into the ro-bot‘s/vehicle's own control and decision-making system not only to understand human behaviour while it occurs but to predict the next actions. To obtain knowledge about the human's intentions, this work relies heavily on the use of motion tracking data (i.e. skeletal tracking, body posture)gathered from drivers whilst driving. We use a data...

Vehicle Dynamics Approach to Driver Warning

International Journal of Vehicular Technology, 2013

This paper discusses a concept for enhanced active safety by introducing a driver warning system based on vehicle dynamics that predicts a potential loss of control condition prior to stability control activation. This real-time warning algorithm builds on available technologies such as the Electronic Stability Control (ESC). The driver warning system computes several indices based on yaw rate, side-slip velocity, and vehicle understeer using ESC sensor suite. An arbitrator block arbitrates between the different indices and determines the status index of the driving vehicle. The status index is compared to predetermined stability levels which correspond to high and low stability levels. If the index exceeds the high stability level, a warning signal (haptic, acoustic, or visual) is issued to alert the driver of a potential loss of control and ESC activation. This alert will remain in effect until the index is less than the low stability level at which time the warning signal will be...

Risk analysis for cooperation between the driver and the control system of an autonomous vehicle

International Review of Mechanical Engineering, 2018

Autonomous vehicles will be a reality in our society shortly, first coming vehicle will come with level 3 of automation where the driver is expected to be available for occasional control either for pleasure or during critical situations. This article proposes a strategy that aims to predict the path desired by the driver through steering actions and the algorithms used by of the autonomous vehicle to utilize risk indicators. These risk indicators can be used to condition the cooperation and the transition from automatic to manual driving. This technique uses the data of a path model predictive controller and local path planning data based on a curvilinear space to predict the driver interaction and intention when he engages the steering wheel. Some results are presented in a processor-in-the-loop environment, which let to verify that driver intention can be integrated with the risk analysis of the autonomous vehicle with minimal computational cost. Some conclusions and future improvements to the system are proposed.

Drivers' Manoeuvre Modelling and Prediction for Safe HRI

ArXiv, 2021

As autonomous machines such as robots and vehicles start performing tasks involving human users, ensuring a safe interaction between them becomes an important issue. Translating methods from human-robot interaction (HRI) studies to the interaction between humans and other highly complex machines (e.g. semi-autonomous vehicles) could help advance the use of those machines in scenarios requiring human interaction. One method involves understanding human intentions and decision-making to estimate the human’s present and near-future actions whilst interacting with a robot. This idea originates from the psychological concept of Theory of Mind, which has been broadly explored for robotics and recently for autonomous and semi-autonomous vehicles. In this work, we explored how to predict human intentions before an action is performed by combining data from human-motion, vehicle-state and human inputs (e.g. steering wheel, pedals). A data-driven approach based on Recurrent Neural Network mod...