The recent computerizations of cars, together with the development of sensor technologies and car communication devices have transformed the cars into wealthy sources of information. The analysis of data generated continuously by cars can contribute greatly in improving driving safety and drivers comfort. Even though different analytical solutions have emerged recently, there still exist some important issues in driving safety that we assume were poorly addressed, as well as diverse mathematical methodologies whose application in driving behavior analysis is to be investigated. In this paper, we developed a methodology to process and analyze car-generated data, with focus on two analysis goals: 1) automatic verification of drivers' behavior conformity to traffic rules; and 2) visualization and comparison of drivers' behaviors. The proposed methodology is divided into three steps. At first, the abstraction using numerical domains is used to reduce the size of the generated data. Then, the probabilistic graphical models (Probabilistic Automata, and Labeled Directed Graphs) combined with a machine-learning algorithm are used for building a formal model of the driver behavior. Finally, two indepth analyses are carried out by applying automatic model checking and graph matching techniques. Early experimental results point out that the design of numerical domains considered influences hugely the analysis results.

Traffic sign recognition (TSR) represents an important feature of advanced driver assistance systems, contributing to the safety of the drivers, pedestrians and vehicles as well. Developing TSR systems requires the use of computer vision techniques, which could be considered fundamental in the field of pattern recognition in general. Despite all the previous works and research that has been achieved, traffic sign detection and recognition still remain a very challenging problem, precisely if we want to provide a real time processing solution. In this paper, we present a comparative and analytical study of the two major approaches for traffic sign detection and recognition. The first approach is based on the color segmentation technique and convolutional neural networks (C-CNN), while the second one is based on the fast region-based convolutional neural networks approach (Fast R-CNN).

Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for on-device training, enabling customized thermal experiences in homes. However, leveraging the flexibility offered by on-device learning has been hindered by the absence of a tailored learning scheme that allows for accurate on-device training of thermal models. Traditional centralized learning (CL) and federated learning (FL) schemes rely on a central server that controls the learning experience, compromising the home’s privacy and requiring significant energy to operate. To address these challenges, we propose PePTM, a personalized peer-to-peer thermal modeling algorithm that generates tailored thermal models for each home, offering a controlled learning experience with a minimal training energy footprint while preserving the home’s privacy, an aspect difficult to achieve in both CL and FL. PePTM consists of local and collaborative learning phases that enable each home to train its thermal model and collaboratively improve it with a set of similar homes in a peer-to-peer fashion. To showcase the effectiveness of PePTM, we use a year’s worth of data from US homes to train thermal models using the RNN time-series model and compare the data across three learning schemes: CL, FL, and PePTM, in terms of model performance and the training energy footprint. Our experimental results show that PePTM is significantly energy-efficient, requiring 695 and 40 times less training energy than CL and FL, respectively, while maintaining comparable performance. We believe that PePTM sets the stage for new avenues for on-device thermal model training, providing a personalized thermal experience with reduced energy consumption and enhanced privacy.

The integration of smart thermostats in home automation systems has created an opportunity to optimize space heating and cooling through the use of machine learning, for example for thermal model identification. Nonetheless, its full potential remains untapped due to the lack of a suitable learning scheme. Traditional centralized learning (CL) and federated learning (FL) schemes could pose privacy and security concerns, and result in a generic model that does not adequately represent thermal requirements and characteristics of each individual home. To overcome these limitations, in this paper we embrace the novel peer-to-peer learning scheme for on-device training of home thermal models. Specifically, we adapt the personalized peer-to-peer algorithm proposed in recent work (called P3) to efficiently train personalized thermal models on resource-constrained devices. Our preliminary experiments with data from 1,000 homes, using the LSTM model, demonstrate that the adapted P3 algorithm produces accurate and personalized thermal models while being extremely energy-efficient, consuming respectively 600 and 40 times less energy than the CL and FL schemes. This result suggests that the P3 algorithm offers a privacy-conscious, accurate, and energy-efficient solution for training thermal models for the many homes in the building stock.