Shinji Nakadai

Sasinee Pruekprasert, Shinji Nakadai

This study introduces a novel Air Traffic Control (ATC) concept to support self-separation between vehicles in Urban Air Mobility (UAM) corridors. Our proposed scheme involves sharing intended arrival schedules at Constrained Waypoints (CWPs) among UAM operators. We propose two approaches to assist the arrival scheduling at CWPs by computing the minimum arrival time gap necessary for each pair of vehicles to ensure their safety throughout the flights within the corridor. The first approach considers the minimum separation distance required by the Near Mid-Air-Collision (NMAC) avoidance rules, while the second one is based on the Responsibility-Sensitive Safety (RSS) rules. We demonstrate that the NMAC-rule-based approach can effectively prevent collisions in normal circumstances, where the vehicles adhere to the speed limits of the corridor. However, this approach does not guarantee safety if vehicles exceed the speed limits. Conversely, while the RSS-rule-based approach ensures collision prevention during emergencies when vehicles exceed speed limits, it may require larger arrival time gaps under normal circumstances, which may lead to reduced traffic flow. Our results are confirmed through numerical simulations.

Akihiro Fujita, Sasinee Pruekprasert, Katsuhiro Nishinari et al.

This paper explores scalable coordination strategies for urban air mobility (UAM) corridors by comparing two representative approaches. The first, inspired by visual flight rules (VFR), is a local coordination strategy relying on spatial information available to each vehicle. The second, conceptually aligned with digital flight rules (DFR), is a global coordination strategy based on shared estimated times of arrival (ETAs) at constrained waypoints (CWPs). To support this comparison, we introduce a lightweight disturbance-avoidance mechanism that enables vehicles to adjust their ETAs in response to forecasted disruptions using shared information. We evaluate these approaches through numerical simulations under varying disturbance levels, comparing the locally reactive VFR-style scheme with the globally coordinated DFR-style scheme. Results show that VFR achieves high throughput in low-traffic scenarios but becomes increasingly prone to collisions at higher traffic densities unless conservative separation is enforced, which reduces traffic efficiency. In contrast, DFR maintains more consistent safety performance and traffic efficiency, even under moderate ETA update propagation delays. These findings highlight the advantages of DFR-style global coordination in managing high-density air traffic control (ATC) operations within UAM corridors.

Sasinee Pruekprasert, Shinji Nakadai, Katsuhiro Nishinari

This paper presents a safety-assured arrival-scheduling framework for Urban Air Mobility (UAM) corridor operations. We propose an analytical method to compute a sufficient ETA gap at Constrained Waypoints (CWPs) that guarantees longitudinal separation along sequential corridor sections with heterogeneous speed limits. The resulting ETA-gap condition depends on section-specific speed bounds and the required separation distance, providing an efficiently computable rule suitable for integration into future digital ETA-scheduling and air traffic management systems. We show that the computed ETA gap ensures safe separation across all corridor sections under prescribed section travel times and speed limits. Numerical simulations for a decreasing-speed corridor confirm that vehicles coordinated with the proposed mechanism adjust their speeds to maintain the required spacing, avoid potential collisions, and support improved traffic flow compared with unscheduled operations.

Kazuma Shimizu, Junya Honda, Shinji Ito et al.

This paper discusses the revenue management (RM) problem to maximize revenue by pricing items or services. One challenge in this problem is that the demand distribution is unknown and varies over time in real applications such as airline and retail industries. In particular, the time-varying demand has not been well studied under scenarios of unknown demand due to the difficulty of jointly managing the remaining inventory and estimating the demand. To tackle this challenge, we first introduce an episodic generalization of the RM problem motivated by typical application scenarios. We then propose a computationally efficient algorithm based on posterior sampling, which effectively optimizes prices by solving linear programming. We derive a Bayesian regret upper bound of this algorithm for general models where demand parameters can be correlated between time periods, while also deriving a regret lower bound for generic algorithms. Our empirical study shows that the proposed algorithm performs better than other benchmark algorithms and comparably to the optimal policy in hindsight. We also propose a heuristic modification of the proposed algorithm, which further efficiently learns the pricing policy in the experiments.

Ayan Sengupta, Yasser Mohammad, Shinji Nakadai

Despite abundant negotiation strategies in literature, the complexity of automated negotiation forbids a single strategy from being dominant against all others in different negotiation scenarios. To overcome this, one approach is to use mixture of experts, but at the same time, one problem of this method is the selection of experts, as this approach is limited by the competency of the experts selected. Another problem with most negotiation strategies is their incapability of adapting to dynamic variation of the opponent's behaviour within a single negotiation session resulting in poor performance. This work focuses on both, solving the problem of expert selection and adapting to the opponent's behaviour with our Autonomous Negotiating Agent Framework. This framework allows real-time classification of opponent's behaviour and provides a mechanism to select, switch or combine strategies within a single negotiation session. Additionally, our framework has a reviewer component which enables self-enhancement capability by deciding to include new strategies or replace old ones with better strategies periodically. We demonstrate an instance of our framework by implementing maximum entropy reinforcement learning based strategies with a deep learning based opponent classifier. Finally, we evaluate the performance of our agent against state-of-the-art negotiators under varied negotiation scenarios.