In this paper we describe cooperation and social dilemmas in multiagent systems, with an analogy applied to road traffic. Cooperative human drivers, based on their perception of trust and fairness, find efficient solutions for such dilemmas. In the development of automated vehicles (AVs) it is therefore important to ensure that this cooperative ability is maintained even without a human driver. Therefore, the topic of cooperative intelligent transport systems (C-ITSs) is discussed in detail and different characteristics of cooperation and their implementation are derived. Further, three planning levels with the corresponding communication techniques are discussed and several methods for maneuver planning are listed. All in all, we hope that this paper will allow us to better classify different cooperative scenarios, develop novel approaches for cooperative AVs (CAVs), and emphasize the need for cooperative driving.

Greenhouse gas emissions are the cause of climate change, which in turn has a negative impact on people and the environment. Reducing the fuel consumption of conventional engines reduces climate-damaging emissions and can, thus, contribute to achieving climate protection goals. In addition, fuel costs are a major cost factor for long-haul trucking. Eco-driving helps to reduce fuel costs when driving on inclines and declines. Due to the high mass and, therefore, high kinetic and potential energy of heavy trucks, fuel can be saved by coasting before slopes and before speed limits. However, energy-efficient and non-cooperative driving, i.e., without considering other road users, can lead to increased fuel consumption as vehicles impede each other. To resolve conflicts in road traffic, a variety of methods that enable cooperative driving exist. In general, vehicles communicate with vehicle-to-everything (V2X) and negotiate a joint driving strategy. This paper presents a method that combines cooperative and energy-efficient driving and examines the impact on fuel consumption during uphill driving. The method relies on the exchange of trajectories for cooperative maneuver coordination. By computing a strategic trajectory, energy-efficient driving with long coasting maneuvers is enabled. In the simulative evaluation, travel over hills with two and three trucks is investigated. It is shown that the combination of cooperative and eco-driving reduces the fuel costs for traffic.

In this paper we investigate cooperative truck overtaking maneuvers on the freeway. First, we explain the necessity of truck overtaking maneuvers and then show their challenges. Through cooperation we try to find approaches that optimize the maneuver in the areas of safety, efficiency and conflicts with cars. We simulated one of our proposed approaches and develop a tool to determine the overtaking points and velocities. Since the velocity profiles of the trucks among other things have to be exchanged for this optimization, we then investigate how this information can be transmitted as compactly as possible. We conclude the paper with the new findings that our approach makes truck overtaking maneuvers more than 50 % faster, reduces the total cost for trucks and cars, and only requires messages of about 264 bits size.

Driving under the influence of alcohol (DUI) remains a major contributor to fatal traffic crashes worldwide. With increasing regulatory pressure, such as requirements by Euro NCAP for in-vehicle impairment detection, there is a growing need for reliable, real-time monitoring solutions. While traditional DUI detection approaches focus on driving behavior or eye movement analysis, this study explores the potential of thermal imaging as a noninvasive alternative for detecting alcohol impairment. We conducted a large-scale experimental study with 120 participants in a high-fidelity driving simulator, capturing thermal facial data under both non-impaired and alcohol-impaired conditions. A novel temperature extraction method was developed based on facial landmarks, incorporating multiple frames to reduce noise and improve temporal stability. Ambient cabin temperature was also recorded to normalize facial temperature readings and control for environmental influences. Several machine learning classifiers, including Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient-Boosting Models, were trained using five facial temperature features (cheek, temple, ear, forehead, and nasal tip) and evaluated via five-fold subject-wise cross-validation. Significant temperature changes were observed in specific facial regions (particularly the cheek, ear, temple, and nasal tip) under alcohol influence. Among the evaluated models, Logistic Regression achieved the highest average classification accuracy (62%), while SVM demonstrated the most stable performance across folds. The model showed a slight conservative bias toward predicting the baseline (non-impaired) class, thereby reducing the risk of false positive classifications. Environmental conditions, including cabin temperature, were verified to be stable across both driving sessions, ensuring the validity of the physiological measurements. This study demonstrates the feasibility of using thermal imaging for in-vehicle DUI detection under realistic conditions. Our contributions include the development of a robust facial temperature processing pipeline, the creation of a unique dataset collected under ecologically valid conditions, and a comprehensive comparison of seven state-of-the-art classification models. Thermal imaging represents a promising complementary modality for future driver monitoring systems focused on safety and impairment detection.