The current understanding of CO₂ emission concentrations in hybrid vehicles (HVs) is limited, due to the complexity of the constant changes in their power-train sources. This study aims to address this problem by examining the accuracy, speed and size of traditional and advanced machine learning (ML) models for predicting CO₂ emissions in HVs. A new long short-term memory (LSTM)-based model called UWS-LSTM has been developed to overcome the deficiencies of existing models. The dataset collected includes more than 20 parameters, and an extensive input feature optimization has been conducted to determine the most effective parameters. The results indicate that the UWS-LSTM model outperforms traditional ML and artificial neural network (ANN)-based models by achieving 97.5% accuracy. Furthermore, to demonstrate the efficiency of the proposed model, the CO₂-concentration predictor has been implemented in a low-powered IoT device embedded in a commercial HV, resulting in rapid predictions with an average latency of 21.64 ms per prediction. The proposed algorithm is fast, accurate and computationally efficient, and it is anticipated that it will make a significant contribution to the field of smart vehicle applications.

Recent advances in state-of-the-art camera-based AI mechanisms in the automated driving field have leveraged great progress in the installation and widespread use of this technology along the recent years. However, vehicles with automated driving capabilities are usually equipped with a wide range of sensors that complement the perception capacity of camera-based AI algorithms. For this reason, this paper tries to reveal the degree of readiness of one of the most used open-source AI models for Level 2 automated driving. To this end, a set of simulated common driving scenarios were used to evaluate the predictions. The results obtained clearly indicate that the current capacity of this camera-based DNN model is not sufficient to be the only source of information in the process of environment perception of a Level 2 automated vehicle, and therefore, further progress in the context awareness needs to be achieved to consider its sole use in the perception stage.

Autonomous driving has gained momentum in recent years. To help create a sustainable future, an autonomous vehicle should take into account its carbon footprint. This work proposes a new, portable, cost-effective carbon dioxide (CO2) emissions sensor installed in a Light Duty Vehicle (LDV), as a novel enabler towards carbon footprint aware autonomous driving. This work achieves the ability to report real-time metrics about the instantaneous CO2 emissions whilst satisfying non-invasive procedures (weight < 1 Kg) by means of Unified Diagnostic Services (UDS) compliant interrogators. The proposed solution is validated in compliance with European Union’s official emissions test procedures, and compared with other commercial off-the-shelf solutions. During the test, a 50 Km trip divided in 3 driving cycles is performed to obtain the instantaneous CO2 emission, achieving a measuring rate similar to other commercial, non-portable and expensive alternatives.