Hassan Jafarzadeh

Minghui Sun, Zhaoyang Chen, Georgios Bakirtzis et al.

Designing systems is typically uncertain and ambiguous at early stages. Set-based design supports alternative exploration and gradual uncertainty reduction during the early lifecycle, making it practical for complex systems design. In parallel, the functional requirements decomposition helps to advance the design incrementally. However, current literature on set-based design lacks formal guidance in how to decompose functional requirements. To bridge this gap, we introduce a four-step method to decompose functional requirements for set-based design hierarchically. We systematically define, reason, and narrow the sets, breaking down the functional requirements into formal sub-requirements. This method allows parallel abstraction, ensuring the resulting system satisfies the top-level functional requirements.

Nazanin Moradinasab, Hassan Jafarzadeh, Donald E. Brown

Domain adaptive semantic segmentation is the task of generating precise and dense predictions for an unlabeled target domain using a model trained on a labeled source domain. While significant efforts have been devoted to improving unsupervised domain adaptation for this task, it is crucial to note that many models rely on a strong assumption that the source data is entirely and accurately labeled, while the target data is unlabeled. In real-world scenarios, however, we often encounter partially or noisy labeled data in source and target domains, referred to as Generalized Domain Adaptation (GDA). In such cases, we suggest leveraging weak or unlabeled data from both domains to narrow the gap between them, resulting in effective adaptation. We introduce the Generalized Gaussian-mixture-based (GenGMM) domain adaptation model, which harnesses the underlying data distribution in both domains to refine noisy weak and pseudo labels. The experiments demonstrate the effectiveness of our approach.

Masoud Bashiri, Hassan Jafarzadeh, Cody Fleming

With the emergence of autonomous ground vehicles and the recent advancements in Intelligent Transportation Systems, Autonomous Traffic Management has garnered more and more attention. Autonomous Intersection Management (AIM), also known as Cooperative Intersection Management (CIM) is among the more challenging traffic problems that poses important questions related to safety and optimization in terms of delays, fuel consumption, emissions and reliability. Previously we introduced two stop-sign based policies for autonomous intersection management that were compatible with platoons of autonomous vehicles. These policies outperformed regular stop-sign policy both in terms of average delay per vehicle and variance in delay. This paper introduces a reservation-based policy that utilizes the cost functions from our previous work to derive optimal schedules for platoons of vehicles. The proposed policy guarantees safety by not allowing vehicles with conflicting turning movement to be in the conflict zone at the same time. Moreover, a greedy algorithm is designed to search through all possible schedules to pick the best that minimizes a cost function based on a trade-off between total delay and variance in delay. A simulator software is designed to compare the results of the proposed policy in terms of average delay per vehicle and variance in delay with that of a 4-phase traffic light.