Recent use scenarios involving human-robot collaboration have revealed that the robots require elastic joints to safely interact with humans. It is also critical to know applied force/torque (f/t) during the interaction for control and motion planning purposes. In this article, we estimate the external f/t values without using any sensors other than low-cost encoders by exploiting the inherent elastic properties of the joint. For estimation, the following two different approaches are used: model based and model free. In the model-based approach, an extended Kalman filter (EKF) and an external force observer (EFOB) are used considering the dynamical behavior of the system to estimate the interaction force. In the model-free approach, the artificial neural network (ANN) utilizes the data gathered from mechanical systems. In comparative analysis, we have, therefore, considered three different estimation methods, two of which are model based and the remaining one is model free (i.e., data driven). Implementing these estimation algorithms experimentally on a variable stiffness joint, we performed an extensive evaluation of their performances. All methods show similar level of performance in terms of the root-mean-square (RMS) error with 0.0847, 0.0841, and 0.1082 N for the EKF, EFOB, and ANN, respectively. Model-based methods do not require continuous data stream through the experimental set up. On the other hand, the ANN does not need an explicit model of the system; therefore, it may become preferable when the detailed model derivation is not possible.

Torque control of electric motor via current gives the advantage of simplicity and fast response over the complicated torque control of an internal combustion engine which may depend on several parameters ranging from fuel valve angle to gas pedal position and several delay factors. Although traction control system (TCS) for in-wheel-motor (IWM) configuration electric vehicles (EV) has advantages, the performance of the control system, as in most traction control cases, still depends on (1)accurate estimation of road-tire friction characteristics and (2) measurement of slip ratio requiring expensive sensors for obtaining wheel and chassis velocity. The main contribution of this work is design and integration of an acoustic road-type estimation system (ARTE), which significantly increases the robustness and reduces the cost of TCS in IWM configuration EVs. Unlike complicated and expensive sensor units, the system uses a simple data collection set-up including a low-cost cardioid microphone directed to vicinity of road-tire interface. The acoustic data is then reduced to features such as linear predictive, cepstrum and power spectrum coefficients. For robust estimation, only some of these coefficients are selected based on minimum intra-class variance and maximum inter-class distance criteria to train an artificial neural network (ANN) for classification. The road types can be grouped into: Asphalt, gravel, stone and snow with a correct classification rate of 91% for the test data. The predicted road-type is used to select the correct friction characteristic curve (μ-λ) which helps calculating the appropriate torque command for the particular road-tire condition. The system has been evaluated in extensive simulations and the results show that extreme torque values are supressed stabilising the vehicle for several driving scenarios in a more energy-efficient and robust manner compared to previous systems.

This paper considers a comprehensive and collaborative project to collect large amounts of driving data on the road for use in a wide range of areas of vehicle-related research centered on driving behavior. Unlike previous data collection efforts, the corpora collected here contain both human and vehicle sensor data, together with rich and continuous transcriptions. While most efforts on in-vehicle research are generally focused within individual countries, this effort links a collaborative team from three diverse regions (i.e., Asia, American, and Europe). Details relating to the data collection paradigm, such as sensors, driver information, routes, and transcription protocols, are discussed, and a preliminary analysis of the data across the three data collection sites from the U.S. (Dallas), Japan (Nagoya), and Turkey (Istanbul) is provided. The usability of the corpora has been experimentally verified with a Cohen's kappa coefficient of 0.74 for transcription reliability, as well as being successfully exploited for several in-vehicle applications. Most importantly, the corpora are publicly available for research use and represent one of the first multination efforts to share resources and understand driver characteristics. Future work on distributing the corpora to the wider research community is also discussed.

Increasing stress levels in drivers, along with their ability to multi task with infotainment systems cause the drivers to deviate their attention from the primary task of driving. With the rapid advancements in technology, along with the development of infotainment systems, much emphasis is being given to occupant safety. Modern vehicles are equipped with many sensors and ECUs (Embedded Control Units) and CAN-bus (Controller Area Network) plays a significant role in handling the entire communication between the sensors, ECUs and actuators. Most of the mechanical links are replaced by intelligent processing units (ECU) which take in signals from the sensors and provide measurements for proper functioning of engine and vehicle functionalities along with several active safety systems such as ABS (Anti-lock Brake System) and ESP (Electronic Stability program). Current active safety systems utilize the vehicle dynamics (using signals on CAN-bus) but are unaware of context and driver status, and do not adapt to the changing mental and physical conditions of the driver. The traditional engine and active safety systems use a very small time window (t<;2sec) of the CAN-bus to operate. On the contrary, the implementation of driver adaptive and context aware systems require longer time windows and different methods for analysis. The long-term history and trends in the CAN-bus signals contain important information on driving patterns and driver characteristics. In this paper, a summary of systems that can be built on this type of analysis is presented. The CAN-bus signals are acquired and analyzed to recognize driving sub-tasks, maneuvers and routes. Driver inattention is assessed and an overall system which acquires, analyses and warns the driver in real-time while the driver is driving the car is presented showing that an optimal human-machine cooperative system can be designed to achieve improved overall safety.