Drive Force and Longitudinal Dynamics Estimation in Heavy-Duty Vehicles.

Modelling the dynamic behaviour of heavy vehicles, such as buses or trucks, can be very useful for driving simulation and training, autonomous driving, crash analysis, etc. However, dynamic modelling of a vehicle is a difficult task because there are many subsystems and signals that affect its behaviour. In addition, it might be hard to combine data because available signals come at different rates, or even some samples might be missed due to disturbances or communication issues. In this paper, we propose a non-invasive data acquisition hardware/software setup to carry out several experiments with an urban bus, in order to collect data from one of the internal communication networks and other embedded systems. Subsequently, non-conventional sampling data fusion using a Kalman filter has been implemented to fuse data gathered from different sources, connected through a wireless network (the vehicle's internal CAN bus messages, IMU, GPS, and other sensors placed in pedals). Our results show that the proposed combination of experimental data gathering and multi-rate filtering algorithm allows useful signal estimation for vehicle identification and modelling, even when data samples are missing.

Human-robot cooperation for robust surface treatment using non-conventional sliding mode control.

This work presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, deburring, etc. The method considers two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The proposed scheme is based on task priority and adaptive non-conventional sliding mode control. The applicability of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Sawyer cobot.

Haptic Feedback to Assist Bus Drivers for Pedestrian Safety at Low Speed.

Buses and coaches are massive Passenger Transportation Systems (PTS), because they represent more than half of land PTS in the European Union. Despite that, bus accident figures are lower than other means of transport, but its size and weight increase the severity of accidents in which buses are involved, even at low speed. In urban scenarios, turnings and maneuvers around bus stops are the main causes of accidents, mostly due to low visibility, blind spots, or driver's distractions. Therefore, there is an increasing interest in developing driving assistance systems to avoid these situations, among others. However, even though there are some solutions on the market, they are not meant to work in urban areas at low speed and with the sole purpose of preventing collisions with pedestrians. In this sense, the paper proposes an active safety system for buses in maneuvers at low speed. The safety system consists of haptic feedback devices together with collision avoidance and risk evaluation systems based on detected people nearby the bus. The performance of the active safety system has been validated in a simulated urban scenario. Our results show that driver's reaction time is reduced and time to collision increased due to the proposed low-speed active safety system. In particular, it is shown that there is a reduction in the number of high risk cases and collisions, which implies a considerable improvement in safety terms. In addition to this, a brief discussion about current regulations for innovative safety systems on real vehicles is carried out.