This work is aimed at the experimental characterisation of air quality and thermal profile within an electric vehicle cabin, measuring at the same time the HVAC system energy consumption. Pollutant concentrations in the vehicle cabin are measured by means of a low-cost system of sensors. The effects of the HVAC system configuration, such as fresh-air and recirculation mode, on cabin air quality, are discussed. It is shown that the PM concentrations observed in recirculation mode are lower than those in fresh-air mode, while VOC concentrations are generally higher in recirculation than in fresh-air mode. The energy consumption is compared in different configurations of the HVAC system. The novelty of this work is the combined measurement of important comfort parameters such as air temperature distribution and air quality within the vehicle, together with the real time energy consumption of the HVAC system. A wider concept of comfort is enabled, based on the use of low-cost sensors in the automotive field.

Unmanned aerial vehicles, and multi-rotors in particular, can now perform dexterous tasks in impervious environments, from infrastructure monitoring to emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark to develop and evaluate these capabilities. Its challenges include accurate and robust visual-inertial odometry during aggressive maneuvers, complex aerodynamics, and constrained computational resources. As researchers increasingly channel their efforts into it, they also need the tools to timely and equitably compare their results and advances. With this dataset, we want to (i) support the development of new methods and (ii) establish quantitative comparisons for approaches originating from the broader robotics and artificial intelligence communities. We want to provide a one-stop resource that is comprehensive of (i) aggressive autonomous and piloted flight, (ii) high-resolution, high-frequency visual, inertial, and motion capture data, (iii) commands and control inputs, (iv) multiple light settings, and (v) corner-level labeling of drone racing gates. We also release the complete specifications to recreate our flight platform, using commercial off-the-shelf components and the open-source flight controller Betaflight, to democratize drone racing research. Our dataset, open-source scripts, and drone design are available at: github.com/tii-racing/drone-racing-dataset .

In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of carbon dioxide (CO₂) being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.

This research addresses the challenges of visually impaired individuals’ independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target objects. Utilizing a monocular camera, the method captures cuboid objects (e.g., fences, pillars) for near-front distance estimation. A Field of View (FOV) model calculates the camera’s angle and arbitrary pitch relative to the target Point of Interest (POI) within the image. Experimental results demonstrate the method’s proficiency in detecting distances between objects and the source camera, employing the FOV and Point of View (POV) principles. The approach achieves a mean absolute percentage error (MAPE) of 6.18% and 6.24% on YOLOv4-tiny and YOLOv4, respectively, within 10 m. The distance model only contributes a maximum error of 4% due to POV simplification, affected by target object characteristics, height, and selected POV. The proposed distance estimation method shows promise in drone racing navigation, EV autopilot, and aiding visually impaired individuals. It offers valuable insights into dynamic 3D environment distance estimation, advancing computer vision and autonomous systems.

Drone technology is proliferating in many industries, including agriculture, logistics, defense, infrastructure, and environmental monitoring. Vision-based autonomy is one of its key enablers, particularly for real-world applications. This is essential for operating in novel, unstructured environments where traditional navigation methods may be unavailable. Autonomous drone racing has become the de facto benchmark for such systems. State-of-the-art research has shown that autonomous systems can surpass human-level performance in racing arenas. However, the direct applicability to commercial and field operations is still limited, as current systems are often trained and evaluated in highly controlled environments. In our contribution, the system's capabilities are analyzed within a controlled environment—where external tracking is available for ground-truth comparison—but also demonstrated in a challenging, uninstrumented environment—where ground-truth measurements were never available. We show that our approach can match the performance of professional human pilots in both scenarios.