This paper proposes a Long Short-Term Memory (LSTM) based approach for accurate State Of Charge (SOC) and State Of Health (SOH) estimation in lithium-ion batteries, which is critical for improving the safety and longevity of electric vehicles. The nonlinear dynamics of batteries, influenced by factors such as temperature, voltage, current, and SOC, poses significant challenges to traditional estimation methods. Using the Long-Term Degradation dataset, our LSTM model captures temporal dependencies and complex electrochemical interactions to predict SOC and SOH under varying operating conditions. The experimental results demonstrate robust performance, with mean squared errors as low as 5.3121×10−5for the estimation of SOC and 5.572×10−5for the estimation of SOH for different current profiles. The proposed framework provides a scalable solution for real-time battery management systems, reducing the reliance on manual feature extraction and enabling generalization across different battery technologies.

This paper investigates the classification of driving styles using unsupervised learning techniques applied to recorded driving data. The study focuses on identifying two primary driving styles: calm and aggressive. The importance of lane change scenarios in discriminating between these styles is highlighted, using features such as lateral speeds and yaw angles. Using spectral clustering and K-means algorithms, a driving style detection method is proposed. The obtained results indicate that K-means outperforms spectral clustering in effectively classifying drivers based on their behaviour, particularly in lane change situations. This research contributes to a deeper understanding of driver behaviour on the road and provides insights into the potential applications of unsupervised learning in driving style recognition.

This review discusses the importance of detecting driver emotions to improve driving safety and user experience. The article presents recent literature on emotion recognition in the context of driving, reviewing different models of emotion representation, recent public databases for driver emotion recognition, and various uni-modal and multi-modal methods to detect driver emotions. The study shows that detecting the driver's emotional state and its intensity is vital to improving driving safety and the user experience, particularly if the emotion is disconnected from or not related to the driving task.

The expectations regarding autonomous vehicles are very high to transform the future mobility and ensure more road safety. Autonomous driving system should be able in the short term to detect dangerous situations and respond appropriately and thus increase driving safety. Understanding the intentions of drivers has recently received growing interest. A long-term prediction method based on gated unit-recurrent neural network model is proposed for the problem of trajectory prediction of surrounding vehicles. A deep neural network with Long-short term memory (LSTM) and Gated Recurrent Units (GRU) structure is used to analyze the spatial-temporal features of the past trajectory. Through sequences learning, the system generates the future trajectory of other traffic participants for different horizons of prediction. We evaluate all models with standard metric (Root mean square error RMSE), loss function convergence and processing time. After comparing the different models, our experiments revealed that the proposed GRU based models is indeed better than LSTM based models in term of accuracy and processing speed.