Abstract Previous work has demonstrated the feasibility of Bluetooth Low Energy (BLE) as an alternative technology for data transfers in inter-vehicular communication (IVC) scenarios. Bluetooth 5.x core specifications enhance the trade-off between energy requirements, communication range and flexibility. In this paper, we aim to analyse the potential of Bluetooth 5 features for VANET applications, proposing a connectionless communication system. By means of field experiments, we evaluate long range and 2 × speed features, defining a set of communication scenarios. This allows us to test both Bluetooth 5.x range and application throughput. The evaluation includes experiments of V2I communications carried out under real highway traffic conditions. The experiments conducted demonstrate that communication ranges up to 300 m may be achieved depending on the communications scenario. The results also show how throughput degrades as the distance between devices increases. The results obtained are used to discuss future work, aimed at deeper analysing Bluetooth 5 features for VANET applications, completing the development of our prototype and evaluating VANET connectionless communications with the features included in the latest Bluetooth 5.2 specification.

In previous studies, we used a method for detecting stress that was based exclusively on heart rate and ECG for differentiation between such situations as mental stress, physical activity, relaxation, and rest. As a response of the heart to these situations, we observed different behavior in the Root Mean Square of the Successive differences heartbeats (RMSSD). This study aims to analyze Virtual Reality via a virtual reality headset as an effective stressor for future works. The value of the Root Mean Square of the Successive Differences is an important marker for the parasympathetic effector on the heart and can provide information about stress. For these measurements, the RR interval was collected using a breast belt. In these studies, we can observe the Root Mean Square of the successive differences heartbeats. Additional sensors for the analysis were not used. We conducted experiments with ten subjects that had to drive a simulator for 25 minutes using monitors and 25 minutes using virtual reality headset. Before starting and after finishing each simulation, the subjects had to complete a survey in which they had to describe their mental state. The experiment results show that driving using virtual reality headset has some influence on the heart rate and RMSSD, but it does not significantly increase the stress of driving.

Road accidents and safe driving are one of the main concerns of transportation systems and the companies that explore different solutions to reduce the accident rate. The most interesting option to achieve this goal is through an on-board training of professional drivers to apply safe driving techniques during their work activity. The purpose of this study is to analyze a monitoring system that is not limited to the real-time vehicle tracking but is also capable of monitoring and providing real-time feedback and in-vehicle training. We analyze the influence of different sociodemographic factors on driving behavior. The analyzed data correspond to an urban public transport company, obtained from a study performed on 246 drivers. The drivers received training based on a blended learning system with an on-board feedback device, accompanied by both theoretical and practical sessions. The driving behavior of each driver is obtained from the data gathered from the vehicles that allow us to characterize their driving patterns. The information related to safe driving is completed with a list of the records of road accidents. The results of the sociodemographic influence on driving behavior provide significant information, giving an elaborated classification of safety driving patterns in order to apply intelligent transportation systems.

The levels of stress while driving affect the way we drive and have an impact on the likelihood of having an accident. Different types of sensors, such as heart rate or skin conductivity sensors, have been previously used to measure stress related features. Estimated stress levels could be used to adapt the driver's environment to minimize distractions in high cognitive demanding situations and to promote stress-friendly driving behaviors. The way we drive has an impact on how stressors affect the perceived cognitive demands by drivers, and at the same time, the perceived stress has an impact on the actions taken by the driver. In this paper, we evaluate how effectively upcoming stress levels can be predicted considering current stress levels, current driving behavior, and the shape of the road. We use features, such as the positive kinetic energy and severity of curves on the road to estimate how stress levels will evolve in the next minute. Different machine learning techniques are evaluated and the results for both intra and inter-city driving and for both intra and inter driver data are presented. We have used data from four different drivers with three different car models and a motorbike and more than 220 test drives. Results show that upcoming stress levels can be accurately predicted for a single user (correlation r = 0.99 and classification accuracy 97.5%) but prediction for different users is more limited (correlation r = 0.92 and classification accuracy 46.9%).

In this paper, we propose a driving assistant that makes recommendations in order to reduce the fuel consumption. The solution only requires a smartphone and an OBD/Bluetooth device. Eco-driving advices try to avoid situations that cause an increase in the fuel consumption such as inappropriate speed or slow reaction to the detection of traffic signs and traffic incidents. The main contribution of this paper is the use of artificial intelligence techniques in order to issue the eco-driving tips that are best adapted to the user profile, the characteristics of the vehicle, and the road state conditions. This is very important because the driver may lose the interest due to the high requirements that tend to be provided by general use eco-driving assistants. In order to properly assess and validate the proposed solution, it has been implemented on several Android mobile devices and has been validated using a dataset of 2,250 driving tests using three different models of vehicles with 25 different drivers on three distinct routes. The results show that the system reduces the fuel consumption by 11.04 percent on average and even, in certain cases, the fuel saving is greater than 15 percent.

In this paper, we propose a mechanism to optimize fuel consumption on regular routes. The idea is to find out in which areas a driver usually realizes inefficient actions from the point of view of energy consumption. The aim is to alert the user in advance in order to adjust the vehicle speed or change gear, avoiding inefficient driving. Unlike other proposals, this solution does not require the driver to change the route in order to save fuel. To detect inefficient areas, the system uses vehicle telemetry: acceleration, deceleration, engine speed, engine load, and vehicle speed. A fuzzy logic system determines whether the driver drove efficiently or not in a region. Then, when the driver drives in the same route, the system predicts if the driver will return to a similar inefficient driving pattern in the nearby region. If the probability is high, the system warns the user. Therefore, the driver can take the appropriate action. The results show that the system reduces the fuel consumption by 7.33% on average and even, in certain cases, the fuel saving is more than 10%.

This paper implements and validates an expert system that, based on the detection or previous knowledge of certain types of traffic signals, proposes a method to reduce fuel consumption by calculating optimal deceleration patterns, minimizing the use of braking. The expert system uses a mobile device's embedded camera to monitor the environment and to recognize certain types of static traffic signals that force or can force a vehicle to stop. The system uses an adaptation of the algorithm proposed by Viola and Jones for the recognition of faces in real time, adapted to the detection of traffic signals. Detected signals are also incorporated into a central database for future use. When the vehicle approaches an upcoming traffic signal, the algorithm estimates the distance required to stop the vehicle without using the brakes, taking into account the rolling resistance coefficient and the road slope angle. Appropriate advice and feedback are provided to the driver to release the accelerator pedal. The expert system is implemented on Android mobile devices and has been validated using a data set of 180 tests with five different models of vehicles and nine different drivers. The main contribution of this paper is the proposal of an assistant that uses information from the environment and from the vehicle to calculate optimal deceleration patterns when approaching traffic signals that force or may force the vehicle to stop. In addition, the proposed solution does not require the installation of infrastructure on the road, and it can be installed into any vehicle.