Exploring the socio - technical challenges confronting the adoption of AI in organisational settings is something that has so far been largely absent from the related literature . In particular, r esearch into requirements for trustworthy AI typically overlooks how people deal with the problems of trust in the tools that they use as part of their everyday work practices . This article presents some findings from an ongoing ethnographic study of how current tools are used in air traffic control work and what it r eveals about requirements for trustworthy AI in air traffic control and other safety - critical application domains.

Traffic congestion remains a significant challenge in modern urban networks. Autonomous driving technologies have emerged as a potential solution. Among traffic control methods, reinforcement learning has shown superior performance over traffic signals in various scenarios. However, prior research has largely focused on small-scale networks or isolated intersections, leaving large-scale mixed traffic control largely unexplored. This study presents the first attempt to use decentralized multi-agent reinforcement learning for large-scale mixed traffic control in which some intersections are managed by traffic signals and others by robot vehicles. Evaluating a real-world network in Colorado Springs, CO, USA with 14 intersections, we measure traffic efficiency via average waiting time of vehicles at intersections and the number of vehicles reaching their destinations within a time window (i.e., throughput). At 80% RV penetration rate, our method reduces waiting time from 6.17s to 5.09s and increases throughput from 454 vehicles per 500 seconds to 493 vehicles per 500 seconds, outperforming the baseline of fully signalized intersections. These findings suggest that integrating reinforcement learning-based control large-scale traffic can improve overall efficiency and may inform future urban planning strategies.

--Traffic congestion remains a major challenge for modern urban transportation, diminishing both efficiency and quality of life. While autonomous driving technologies and reinforcement learning (RL) have shown promise for improving traffic control, most prior work has focused on small-scale networks or isolated intersections. Large-scale mixed traffic control, involving both human-driven and robotic vehicles, remains underexplored. In this study, we propose a decentralized multi-agent reinforcement learning framework for managing large-scale mixed traffic networks, where intersections are controlled either by traditional traffic signals or by robotic vehicles. We evaluate our approach on a real-world network of 14 intersections in Colorado Springs, Colorado, USA, using average vehicle waiting time as the primary measure of traffic efficiency. We are exploring a problem that has not been sufficiently addressed: Is large-scale Multi-Agent Traffic Control (MTC) still feasible when facing time-varying Origin-Destination (OD) patterns?

Neural networks are the basic machine learning architecture behind Deep Learning models. Deep Learning is at the forefront of Artificial Intelligence systems which is solving unsolved complex problems and providing path breaking innovation due to its ability to learn on its own, just like a human brain. More breakthroughs are expected with Neural networks as the computing infrastructural performance capacity further increases. For example, the recent breakthrough in Generative AI is based on Deep Learning models which have been trained on huge amounts of data on enormous infrastructure. However, if there is a need to train and run a Deep Learning system on huge compute infrastructure in Aerospace without tolerance for any delay and with lots of data being acquired continuously on the fly then currently there is no solution available. In future, having this capability to run large deep learning systems in Aerospace can help to create innovative solutions: 1. Increase the capacity of air traffic by establishing Airborne Air Traffic Control Systems which use Deep Learning models to direct each airborne vehicle 2. Process sensor data on the fly to do new findings and more accurate and fast weather predictions 3. Process imaging data on the fly to do new findings and more accurate geographical predictions 4. Process geospatial data on the fly to do new findings Many more kinds of innovative solutions can be built if the capacity to run large neural networks with large data can be achieved in Aerospace.

The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.

The speech echo, in particular, is a specific overlapping phenomenon generated by the A TC communication between the sent and received A TCO speech. Another component of noise, radio transmission noise, is the result of A TC being transmitted over High Frequency (HF), V ery High Frequency (VHF) or Ultra High Frequency (UHF) bandwidths that are susceptible to noise caused by static, radio frequency interference, or thermal noise [38]. Auditory information is encoded using amplitude modulation (AM) because it is less susceptible to the capture effect than frequency modulation (FM) [40]. However, AM signals are also less robust against noise and interference, which has been shown to degrade the performance of ASR models [41]. Due to the complex amalgamation of noises, A TC speech often sounds muffled and unintelligible with a low signal-to-noise ratio (SNR). This poses a problem to ASR systems as the noise will either have to be learned by the ASR model or removed through de-noising measures in pre-processing. However, studies have also shown that the use of de-noisers and enhancers could potentially also result in degraded performance in ASR models, making ASR transcription a tricky problem to address [25].

This paper presents a mixed traffic control policy designed to optimize traffic efficiency across diverse road topologies, addressing issues of congestion prevalent in urban environments. A model-free reinforcement learning (RL) approach is developed to manage large-scale traffic flow, using data collected by autonomous vehicles to influence human-driven vehicles. A real-world mixed traffic control benchmark is also released, which includes 444 scenarios from 20 countries, representing a wide geographic distribution and covering a variety of scenarios and road topologies. This benchmark serves as a foundation for future research, providing a realistic simulation environment for the development of effective policies. Comprehensive experiments demonstrate the effectiveness and adaptability of the proposed method, achieving better performance than existing traffic control methods in both intersection and roundabout scenarios. To the best of our knowledge, this is the first project to introduce a real-world complex scenarios mixed traffic control benchmark. Videos and code of our work are available at https://sites.google.com/berkeley.edu/mixedtrafficplus/home

This report examines the effect of mixed traffic, specifically the variation in robot vehicle (RV) penetration rates, on the fundamental diagrams at unsignalized intersections. Through a series of simulations across four distinct intersections, the relationship between traffic flow characteristics were analyzed. The RV penetration rates were varied from 0% to 100% in increments of 25%. The study reveals that while the presence of RVs influences traffic dynamics, the impact on flow and speed is not uniform across different levels of RV penetration. The fundamental diagrams indicate that intersections may experience an increase in capacity with varying levels of RVs, but this trend does not consistently hold as RV penetration approaches 100%. The variability observed across intersections suggests that local factors possibly influence the traffic flow characteristics. These findings highlight the complexity of integrating RVs into the existing traffic system and underscore the need for intersection-specific traffic management strategies to accommodate the transition towards increased RV presence.

Extreme weather events and other vulnerabilities are causing blackouts with increasing frequency, disrupting traffic control systems and posing significant challenges to urban mobility. To address this growing concern, we introduce \model{}, a naturalistic driving dataset collected during blackouts at complex intersections. Beacon provides detailed traffic data from two unsignalized intersections in Memphis, TN, including timesteps, origin, and destination lanes for each vehicle over four hours. We analyze traffic demand, vehicle trajectories, and density across different scenarios. We also use the dataset to reconstruct unsignalized, signalized and mixed traffic conditions, demonstrating its utility for benchmarking traffic reconstruction techniques and control methods. To the best of our knowledge, Beacon could be the first public available traffic dataset that captures naturalistic driving behaviors at complex intersections.

Managing mixed traffic comprising human-driven and robot vehicles (RVs) across large-scale networks presents unique challenges beyond single-intersection control. This paper proposes a reinforcement learning framework for coordinating mixed traffic across multiple interconnected intersections. Our key contribution is a neighbor-aware reward mechanism that enables RVs to maintain balanced distribution across the network while optimizing local intersection efficiency. We evaluate our approach using a real-world network, demonstrating its effectiveness in managing realistic traffic patterns. Results show that our method reduces average waiting times by 39.2% compared to the state-of-the-art single-intersection control policy and 79.8% compared to traditional traffic signals. The framework's ability to coordinate traffic across multiple intersections while maintaining balanced RV distribution provides a foundation for deploying learning-based solutions in urban traffic systems.