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This paper describes the development and simulation of a voice and pointing gesture interaction system for on-route update of autonomous vehicles’ path. The objective of this research is to provide users of autonomous vehicles a human vehicle interaction mode that enables them to make and communicate spontaneous decisions to the autonomous car, modifying its pre-defined autonomous route in real-time. For example, similar to giving directions to a taxi driver, a user will be able to tell the car «Stop there» or «Take that exit». In this way, the user control/spontaneity vs interaction flexibility dilemma that current autonomous vehicle concepts have, could be solved, potentially increasing the user acceptance of this technology. The system was designed following a level structured state machine approach. The simulations were developed using MATLAB and VREP, a robotics simulation platform, which has accurate vehicle and sensor models.

This article presents a pointing gesture-based point of interest computation method via pointing rays’ intersections for situated awareness interactions in vehicles. The proposed approach is compared with two alternative methods: (a) a point of interest identification method based on the intersection of the pointing ray with the point cloud (PoC) resulting from the vehicle sensors, and (b) the traditional ray-casting approach, where the point of interest is computed based on the first intersection of the pointing rays with locations stored in a 2D annotated map. Simulation results show that the presented method outperforms by 36.25% the traditional ray casting one. However, as it was expected, the sensor-based computation method is more accurate. The validation of our approach was conducted by experiments performed in a test track facility.

In the next 20 years fully autonomous vehicles are expected to be in the market. The advance on their development is creating paradigm shifts on different automotive related research areas. Vehicle interiors design and human vehicle interaction are evolving to enable interaction flexibility inside the cars. However, most of today’s vehicle manufacturers’ autonomous car concepts maintain the steering wheel as a control element. While this approach allows the driver to take over the vehicle route if needed, it causes a constraint in the previously mentioned interaction flexibility. Other approaches, such as the one proposed by Google, enable interaction flexibility by removing the steering wheel and accelerator and brake pedals. However, this prevents the users to take control over the vehicle route if needed, not allowing them to make on-route spontaneous decisions, such as stopping at a specific point of interest.

This paper presents the design of an Eco-Driving Assistant System (EDAS) in which the main goal is to minimize the energy use of battery electric vehicles, in particular, vehicles utilized for public transportation. The system optimizes the speed profile of a real route schedule while satisfying the constraints imposed on speed and time. It includes a driver feedback and a driver scoring GUI which allows the driver improving his/her driving skills and comparing him/herself to a “theoretical perfect driver”. The system also includes a backward simulator that generates information related to the vehicle operation under the particular route to be optimized. The output information from the simulator is used as an input to the optimization algorithm. The simulator was validated using real data from a battery electric vehicle. The EDAS system was tested for three different driving profiles and energy consumption reductions of up to 30.33% were achieved.

This paper presents the design, implementation and validation of a forward simulator for a battery electric bus, developed in MATLAB/Simulink. This simulator allows performing energy consumption analyses for different bus routes. In addition, a user can modify some parameters that affect the powertrain operation to understand their influence in the energy consumption of the bus. These analyses allow the electric bus manufacturers to adapt their powertrain designs and control strategies for different transit agencies with different routes and energy requirements. The simulator was validated using real data from a battery electric bus. The results showed a good correlation between the real and the simulated data. In particular, the absolute error between the real and the simulated State of Charge (SOC), which is one of the most important parameters for this kind of vehicles, was 3.24%.