Self-driving baggage tractors on airport ramps or aprons enable better airport operation procedures and support the expansion of the aviation market. Airport ramps have unique mobility requirements in terms of layout, population, demand, and patterns. Avoiding aircraft movement on an airport apron is a top priority because of critical security and safety issues. Existing aircraft detection approaches use remote-sensing images or surveillance cameras. However, these are not compatible with sensors for low-height equipment at airport ramps. Similarly, public road-based self-driving studies have not considered detecting the massive size and concave contours of movable objects. Camera sensors cannot accurately measure the distance of concave contours, whereas a lidar sensor cannot easily cluster or classify an object among point cloud data. In this paper, we present the fusion of cameras and lidar sensors for aircraft and object detection at airport ramps. We use parallel detection from lidar and camera sensors and then integrate both detection results to compensate for any issues. Using the proposed energy optimization model by adapting a conditional random field, we can handle over- and under-segmentation of the point cloud objects caused by the sparse point cloud generated by the aircraft. Our algorithm achieves 31.1% improvement on tracking and 5.5% improvement on classification over other fusion algorithms when applied to a dataset acquired from the Cincinnati and Northern Kentucky airport.

A highly accurate and robust real-time localization process is crucial for autonomous driving applications. Numerous methods for localization have been proposed, which combine various kinds of input, such as data from environmental sensors, inertial measurement units (IMU), and the Global Positioning System (GPS). Because reliance on a single environmental sensor is a vulnerable approach, the use of multiple environmental sensors is a better alternative. However, the fusion methods from previous studies have not adequately compensated for the drawbacks due to the lack of sensor diversity nor have the methods considered the fail-safe issue. In this paper, we propose a multi-modal fusion-based localization framework that uses multiple map matching sources. The framework contains two independent map matching sources and integrates them in a stochastic situational analysis model. By applying a probabilistic model, the more reliable map matching between the multiple sources is determined and the system stability is verified via a fail-safe action. A number of experiments with autonomous vehicles within actual driving environments have shown that combining multiple map matching sources yield more robust results than the use of a single map matching.

Self‐driving baggage tractors driving on airport ramps/aprons present new trends that promote better airport operation procedures and proliferate the aviation market. Airport ramps have unique mobility requirements when it comes to layout, population, demand, and patterns. Estimating aircraft movement is highly crucial and must be required because of safety reasons and airport operations rules. The movement of aircraft at the airport ramp is not dynamic but relatively static and slow. However, it is more complicated in estimating attributes. Even though the aircraft is parked and stationary, all operational vehicles should be cautious on aircraft pushing in or out from the parking stand. However, this is never dealt with in any research since aircraft detection observed at the airport ramp is not suggested in previous research. In this paper, a context‐aware fusion‐based method for aircraft intention detection at airport ramps is proposed. A parallel extraction of behavioural features from the aircraft and situational context detection from other adjacent objects are suggested. Using the proposed method, aircraft can be detected and aircraft movement context can be estimated. The feasibility of this algorithm is demonstrated based on the fluent dataset obtained at Cincinnati Airport.

Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve the efficiency of traffic management on highways. Due to the fact that this type of classification problem has scarcely been proposed, we have adapted various classification algorithms and corresponding datasets specifically designed for detecting special traffic events. By utilizing datasets containing data on road debris and malfunctioning or crashed vehicles obtained from Korean highways, we demonstrate the feasibility of our algorithms. Our main contributions encompass a thorough adaptation of various deep-learning algorithms and class definitions aimed at detecting actual emergencies on highways. We have also developed a dataset and detection algorithm specifically tailored for this task. Furthermore, our final end-to-end algorithm showcases a notable 9.2% improvement in performance compared to the object accident detection-based algorithm.

With advances in the technology applied to automated driving systems (ADSs), active efforts have been made to evaluate the safety of ADS in various complex situations using simulations. In accordance with these efforts, numerous institutions have developed single-scenario pools that reflect a variety of road and traffic characteristics and ADS performances. However, a single scenario has limitations in comprehensively evaluating the performance of complex ADS. Therefore, this study proposed a methodology that combines and transforms single scenarios into multiple scenarios. This aided in continuously evaluating the ADS performance over entire road segments and implemented this methodology in the simulations.

Self-driving baggage tractors on airport ramps signify emerging trends for enhancing operational procedures at airports and contribute to the growth of the aviation industry. Airport ramps are characterized by unique mobility requirements, including layout, population, demand, and traffic patterns. Among these, accurate estimation of aircraft movements is paramount for safety and compliance with airport operational regulations. Contrary to the dynamic nature of other environments, airport ramps have predominantly static and slow movements. Even when an aircraft is parked and stationary, other operational vehicles must exercise caution or halt when the airplane is preparing to push back from or into a parking space. This aspect of airport operations has not been addressed adequately in existing research, and prior studies have rarely considered the detection of airplanes on ramps. This work introduces a context-aware multimodal approach for detecting airplane intentions on airport ramps using RGB and thermal cameras. The proposed methodology involves parallel extraction of behavioral features from airplanes and situational context from their surrounding objects. This approach enables estimation of the movement attributes of the aircraft in relation to other objects on the ramp. The effectiveness of this algorithm was validated through a comprehensive dataset collected from the Cincinnati and Northern Kentucky Airport using the proposed platform. Accordingly, the performance improved by 15.76% with the proposed modality through the use of the thermal camera and additionally by 7.29% through utilization of the proposed network.