Automatic pain recognition from facial expressions is a challenging problem that has attracted a significant attention from the research community. This article provides a comprehensive analysis on the topic by comparing some popular and Off-the-Shell CNN (Convolutional Neural Network) architectures, including MobileNet, GoogleNet, ResNeXt-50, ResNet18, and DenseNet-161. We use these networks in two distinct modes: stand alone mode or feature extractor mode. In stand alone mode, the models (i.e., the networks) are used for directly estimating the pain. In feature extractor mode, the “values” of the middle layers are extracted and used as inputs to classifiers, such as SVR (Support Vector Regression) and RFR (Random Forest Regression). We perform extensive experiments on the benchmarking and publicly available database called UNBC-McMaster Shoulder Pain. The obtained results are interesting as they give valuable insights into the usefulness of the hidden CNN layers for automatic pain estimation.

Due to the sensitivity of the information exchanged in Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication, generating secret keys is critical to secure these communications. As nature is open access, distributed keys are more vulnerable to attacks in the vehicular environment. Physical layer key generation methods using wireless channel characteristics show promise in preventing such attacks, generating keys independently, and removing the need for distribution. In this paper, we present a novel key generation approach in a real vehicular environment based on Channel State Information (CSI), including a new algorithm for key bit extraction. We implemented our algorithm using USRP B210 Software-Defined Radios (SDR) and the industry-standard V2X communication protocol: IEEE 802.11p. The proposed key generation protocol uses the CSI values of each sub-carrier as a source of randomness, from which bits are extracted using a new QAM demodulator quantizer (QAM-Dem-Quan). We compared our technique to state-of-the-art Received Signal Strength (RSS)-based approaches, and show that our method achieves better performance. Moreover, we reached a min-entropy of approximately 70% for the generated keys and a key generation rate of less than $150~\mu \text{s}$ /key for key lengths ranging from 16 to 128 bits.

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a important for performing complex driving maneuvers. Classical GNSS-based methods are usually not accurate enough to have lane-level localization to support the AV’s maneuvers. LiDAR-based localization can provide accurate localization. However, the price of LiDARs is still one of the big issues preventing this kind of solution from becoming wide-spread commodity. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system and a low-cost GPS to achieve high precision lane-level localization. Experiments in real-world and real-time demonstrate that the proposed method achieves good lane-level localization accuracy, outperforming solutions based on only GPS.

Vehicular ad hoc networks allow vehicles to share their information for the safety and efficiency of traffic purposes. However, information sharing can threaten the driver’s privacy as it includes spatiotemporal information, and the messages are unencrypted and broadcasted periodically. Therefore, they cannot estimate their privacy level because it also depends on their surroundings. This article proposes a centralized adaptive pseudonym change scheme that permits the certificate’s authority to adjust the pseudonyms assignment for each requesting vehicle. This scheme adapts dynamically depending on the density of the traffic environment and the user’s privacy level, and it aims to solve the trade-off problem between wasting pseudonyms and Sybil attack. We employ a Knapsack problem-based algorithm for target tracking and an entropy-based method to measure each vehicle’s privacy. In order to demonstrate the applicability of our framework, we use real-life data captured during the interoperability tests of the European project InterCor. According to the experimental results, the proposed scheme could easily estimate the level of confidentiality and, therefore, may best respond to the adaptation of the pseudonyms.

Vehicular communications have become essential for functional and road safety purposes due to the development of vehicle fleets. However, these communications have made vehicles more vulnerable to cyber-attacks. The security of data exchanges in vehicular networks currently relies on a centralized architecture that is responsible for managing various security services such as authentication, confidentiality, non-repudiation, real-time misbehavior detection, certificate management and revocation. However, this centralized approach can be challenging and costly for authorities, and it may even weaken the network's overall security. In this paper, we propose a complementary solution that uses a decentralized security framework to help authorities better manage their network security by involving each vehicle in the overall security management. Our framework, TileChain, is blockchain based solution that use road information to manage the network's security dynamically. The architecture is designed to optimize any security service efficiently. To demonstrate the feasibility and performance of our proposed solution, we selected certificate revocation as a critical service and performed simulations using real vehicle traffic data provided by The French road operator DIR Nord. Our Smart Contract achieved an accuracy of 81.3% in misbehavior detection, leading to certificate revocation. Furthermore, the computation load for security management by authorities potentially reduced by a factor of 96.8%. In summary, our proposed solution, TileChain, offers a promising decentralized approach for managing vehicular network security that can potentially improve the overall security of the network while reducing the computational burden on authorities.