As autonomous driving (AD) systems grow more complex, their rising computational demands pose significant energy and sustainability challenges. This paper investigates spiking neural networks (SNNs) as low‐power alternatives to convolutional neural networks (CNNs) for regression tasks in AD. We introduce a membrane‐potential ( V mem ) decoding framework that converts binary spike trains into continuous outputs and propose the energy‐to‐error ratio (EER), a unified metric combining prediction error with energy consumption. Three CNN architectures (PilotNet, LaksNet, and MiniNet) and their corresponding SNN variants are trained and evaluated using delta, latency, and rate encoding across varied parameter settings, with energy use and emissions logged. Delta‐encoded SNNs achieve the highest EER, substantial energy savings with minimal performance loss, whereas CNNs, despite slightly better MSE, incur 10–20 × higher energy costs. Rate encoding underperforms, and latency encoding, though improving relative error, demands excessive energy. Parameter tuning (threshold θ , temporal dynamics ( S ), membrane time constant ( τ ), and gain G ) directly influences eco‐efficiency. All experiments run on standard GPUs, showing SNNs can surpass CNNs in eco‐efficiency without specialized hardware. Paired statistical tests confirm that only delta‐encoded SNNs achieve significant EER improvements. This work presents a practical, energy‐aware evaluation framework for neural architectures, establishing EER as a critical metric for sustainable machine learning in intelligent transport and beyond.

Autonomous driving and the machine learning (ML) models developed to achieve it have grown rapidly in importance and complexity respectively, and with them has also grown their carbon footprint due to long training times. Given the importance of climate change, it should be necessary to include the CO2 impact of ML models explicitly to encourage competition on more than just model quality. This work presents the implementation of two different Convolutional Neural Network (CNN) training approaches, used for autonomous driving by behavioral cloning in a simulated environment and compares their impact on the CO2 footprint. Using a cloud execution environment and driving data, previously obtained by applying an end-to-end deep learning technique, the first implemented training approach is a classical approach that uses an image generator that carries out the pre-processing and data augmentation during training. The contribution proposed in this paper is a second approach that improves the training by decreasing the training time, carrying out the data augmentation and pre-processing tasks before training the model, storing the result in RAM and then starting the training. The new approach to training presented in this article finishes the training approximately 38 times faster and reduces the carbon footprint impact by approximately 96%. In absolute values, this is a reduction from an average value of approximately 0.1643 CO2eq (kg) to 0.007 CO2eq (kg). To estimate the impact of CO2, the hardware used for the project, the training time and the cloud service provider were all taken into account.

In this paper, we tackle the problem of graph generalization in the context of privacy-preserving social network mining. By grouping together nodes that are not only similar but that also belong to the same k-shells, we better preserve the community structure of the graph, its utility in case of clustering-related applications, while still achieving some privacy level through the concept of graph generalization. We conduct empirical evaluations of our approach on synthetic and real social network data, demonstrating its utility and practical application.

In this paper, we consider the problem of anonymization on large networks. There are some anonymization methods for networks, but most of them can not be applied on large networks because of their complexity. We present an algorithm for k-degree anonymity on large networks. Given a network G, we construct a k-degree anonymous network, G, by the minimum number of edge modifications. We devise a simple and efficient algorithm for solving this problem on large networks. Our algorithm uses univariate micro-aggregation to anonymize the degree sequence, and then it modifies the graph structure to meet the k-degree anonymous sequence. We apply our algorithm to a different large real datasets and demonstrate their efficiency and practical utility.