You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper introduces a hierarchical cellular automaton (HCA)model for simulation of distributed self-organizing control of traffic signals at intersections in road network. The proposed HCA consists of three hierarchy levels that describe the movement of particular vehicles, occupancy of traffic lanes, and signal phases at intersections. Update rule of the HCA was designed to control traffic signals and minimize delays of vehicles in the road network. The introduced update rule takes into account states of cells from different hierarchy levels of the HCA that represent neighboring traffic lanes and intersections. Simulation experiments were conducted for a wide range of traffic conditions - from free flow to saturated traffic in two scenarios: anhattan-like grid road network, and arterial road. Results of the simulations show that the proposed HCA-based traffic control strategy achieves better effectiveness in comparison with the state-of-the-art back pressure algorithm.

Cited Abstract(s):
[1]: This paper introduces the concept of hierarchical cellular automata (HCA). The theory of HCA is developed over the Galois extension field GF(2(pqr..)), where each cell of the CA can store and process a symbol in the extension field GF (2(pqr..)). The hierarchical field structure of GF(2(pqr..)) is employed for design of an HCA-based test pattern generator (HCATPG). The HCATPG is ideally suited for testing very large scale integration circuits specified in hierarchical structural description. Experimental results establish the fact that the HCATPG achieves higher fault coverage than that which could be achieved with any other test structures. The concept of percentile improvement in fault coverage is introduced to have a realistic assessment of fault coverage achieved with the proposed RCATPG.

[2]: Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. In this paper, we introduce Hierarchical Cellular Automata (HCA) -- a temporally evolving model to intelligently detect salient objects. HCA consists of two main components: Single-layer Cellular Automata (SCA) and Cuboid Cellular Automata (CCA). As an unsupervised propagation mechanism, Single-layer Cellular Automata can exploit the intrinsic relevance of similar regions through interactions with neighbors. Low-level image features as well as high-level semantic information extracted from deep neural networks are incorporated into the SCA to measure the correlation between different image patches. With these hierarchical deep features, an impact factor matrix and a coherence matrix are constructed to balance the influences on each cell's next state. The saliency values of all cells are iteratively updated according to a well-defined update rule. Furthermore, we propose CCA to integrate multiple saliency maps generated by SCA at different scales in a Bayesian framework. Therefore, single-layer propagation and multi-layer integration are jointly modeled in our unified HCA. Surprisingly, we find that the SCA can improve all existing methods that we applied it to, resulting in a similar precision level regardless of the original results. The CCA can act as an efficient pixel-wise aggregation algorithm that can integrate state-of-the-art methods, resulting in even better results. Extensive experiments on four challenging datasets demonstrate that the proposed algorithm outperforms state-of-the-art conventional methods and is competitive with deep learning based approaches.
Other application areas of HCA include electronics and computer vision. have employed a HCA to design a test pattern generator for testing very large scale integration circuits specified in hierarchical structural description [1] . The HCA in that study was designed based on the theory of the extension field. In [2] the Authors have introduced a HCA, which consists of single-layer cellular automata and cuboid cellular automata. That HCA was used to detect salient objects, i.e., the most important parts of an image.