Mode Decision Research Articles

Target Vehicle Motion Prediction-Based Motion Planning Framework for Autonomous Driving in Uncontrolled Intersections

This paper presents a motion-planning framework for urban autonomous driving at uncontrolled intersections. The intention and future state of the target vehicles are predicted using information obtained from the environment sensors. The target state prediction module employs an Interacting Multiple Model (IMM) filter to infer the intention of targets. The prediction results of each model of the IMM filter are fused to predict the future state of targets. The proposed predictor uses the intelligent driver model based-driver behavior model to construct the local filter of IMM. The driving mode decision is realized as a state machine consisting of two phases, ‘Approach’ and ‘Risk Management’. The risk management phase is composed of two sub-modes, ‘Cross’ and ‘Yield’. The state transition conditions between phases and modes are defined by introducing the concepts of ‘Critical gap’ and ‘Follow-up gap’. Based on the determined driving mode, the motion planning module consists of two sub-modules for each phase. The required deceleration determination for the approach phase is proposed to consider the occluded region in order to prevent inevitable collisions caused by fast approaches. The model predictive controller for the risk management phase is designed to determine the desired acceleration to guarantee safety and prevent unnecessary deceleration simultaneously. Both computer simulation studies and vehicle tests are conducted to evaluate the proposed framework. The results indicate that the proposed framework ensures the safety at uncontrolled intersections with a driving pattern similar to that of a driver.

A CNN-Based Fast Inter Coding Method for VVC

The Versatile Video Coding (VVC) achieves superior coding efficiency as compared with the High Efficiency Video Coding (HEVC), while its excellent coding performance is at the cost of several high computational complexity coding tools, such as Quad-Tree plus Multi-type Tree (QTMT)-based Coding Units (CUs) and multiple inter prediction modes. To reduce the computational complexity of VVC, a CNN-based fast inter coding method is proposed in this paper. First, a multi-information fusion CNN (MF-CNN) model is proposed to early terminate the QTMT-based CU partition process by jointly using the multi-domain information. Then, a content complexity-based early Merge mode decision is proposed to skip the time-consuming inter prediction modes by considering the CU prediction residuals and the confidence of MF-CNN. Experimental results show that the proposed method reduces an average of 30.63% VVC encoding time, and the Bjøontegaard Delta Bit Rate (BDBR) increases about 3%.

Fast CU Partition Decision Method Based on Bayes and Improved De-Blocking Filter for H.266/VVC

The H.266/Versatile Video Coding (VVC) standard is the latest video coding standards released by the Joint Video Exploration Group (JVET). These new technologies have brought more than 40% increase in compression rate and brought huge coding complexity. In order to reduce the coding complexity of VVC, we propose a fast Coding Unit (CU) partition decision algorithm combining Bayes algorithm and Improved De-blocking Filter (IDBF). The algorithm includes two stages. In the parameter update stage, the original VVC Test Model (VTM) is used to encode every certain video frame, and the Rough Mode Decision evaluation cost (JRMD) generated during the encoding process is used to update the parameters of the Bayes algorithm. In the fast coding stage, the video frame between two parameter update frames is encoded. First, the JRMD of the current CU is calculated and compares it with the split threshold to decide whether the current CU continues to split. If the current CU continues to be split, the current CU is divided into the same 16 simulation blocks, and the IDBF is used to calculate the texture direction and texture complexity of each simulation block. According to the texture features contained in the simulation block, the texture distribution position and texture complexity in the current CU can be judged, so that some bad candidate splitting modes can be discarded. Experimental results show that the proposed method can reduce the complexity of VVC about 56.08%, while Bjontegaard delta bit rate (BDBR) only increases by 1.30%.

Efficient Prediction Mode Decisions for Low Complexity MV-HEVC

The increased number of possible inter-prediction blocks sizes, improved the coding efficiency of High Efficiency Video Coding (HEVC), but at the same time it also increased the complexity of the encoder. In this work, we propose an Efficient Inter-Prediction Mode Decision (EIPMD) technique for the Multi-View extension of the HEVC standard (MV-HEVC). We figure out the least and most probable inter-prediction Modes and use Coding Unit (CU) splitting information of the base view to find its relation with the prediction Modes. We improve the efficiency of our technique by optimizing the base view search area. In our technique, we also makes use of the current depth level of the CU and temporal level of the pictures. Simulation results show that compared to HTM [16.2], which is the reference encoder for MV-HEVC, our proposed technique reduces the encoding time by approximately 66% on average, with very minor change in other parameters.

Model-Based Joint Bit Allocation Between Geometry and Color for Video-Based 3D Point Cloud Compression

Rate distortion optimization plays a very important role in image/video coding. But for 3D point cloud, this problem has not been investigated. In this paper, the rate and distortion characteristics of 3D point cloud are investigated in detail, and a typical and challenging rate distortion optimization problem is solved for 3D point cloud. Specifically, since the quality of the reconstructed 3D point cloud depends on both the geometry and color distortions, we first propose analytical rate and distortion models for the geometry and color information in video-based 3D point cloud compression platform, and then solve the joint bit allocation problem for geometry and color based on the derived models. To maximize the reconstructed quality of 3D point cloud, the bit allocation problem is formulated as a constrained optimization problem and solved by an interior point method. Experimental results show that the rate-distortion performance of the proposed solution is close to that obtained with exhaustive search but at only 0.68% of its time complexity. Moreover, the proposed rate and distortion models can also be used for the other rate-distortion optimization problems (such as prediction mode decision) and rate control technologies for 3D point cloud coding in the future.

H.266/VVC fast intra prediction using Sobel edge features

Versatile video coding is the next-generation video coding standard. Compared with the previous high efficiency video coding standard, versatile video coding reduces 30% or more bitrate but requires significantly higher encoding complexity. This Letter proposes a fast intra prediction algorithm for partition type selection and mode decision of versatile video coding. The Sobel operator is employed to extract the gradient intensity and edge direction of coding blocks. Early skip conditions for partition types are developed based on the dominant edge indicated by block gradients. The intra prediction modes are further restricted to the directions close to the edge orientation of a coding block. Experimental results indicate that the proposed method in the Intra-only configuration saves 34.66% of the encoding time with a slight (0.61%) increase in Bjøntegaard delta bit rate, which achieves the best tradeoff between acceleration and quality among all the investigated methods.

Early SKIP Mode Decision for HEVC with Hierarchical Coding Structure

Early SKIP Mode Decision for HEVC with Hierarchical Coding Structure

Fast Intra Mode Decision Algorithm for HEVC Based on Block Textural Properties

In order to achieve greater coding efficiency compared with the previous video coding standards, various advanced coding techniques are used in the High Efficiency Video Coding (HEVC) standard, such as a flexible partition and a large number of intra prediction modes. However, these techniques lead to much greater complexity that restricts HEVC from realtime applications. To solve this problem, a fast intra mode decision algorithm is proposed in this paper that uses the block’s textural properties to determine the partition depth range and decide whether to split or skip smaller sizes of the coding unit. Besides that, the number of candidate modes for the rough mode decision process is also reduced depending on the block’s property. Experimental results for the recommended test sequences by the JCT-VC show that the proposed algorithm can save an average of 44% encoder time with a slight loss in performance compared to the reference software HM-16.20.

How habit moderates the commute mode decision process: integration of the theory of planned behavior and latent class choice model

This study attempts to develop a comprehensive framework by integrating the theory of planned behavior (TPB) and latent class choice model, with aim to understanding how mode-use habits moderate the process underlying commute mode choice. By designating habit as the covariate in the class membership model, three segments with unique mode-use habit style are obtained. First, heterogeneity in the effects of socio-demographic variables and TPB-related cognitive factors on commute mode choice across segments are empirically confirmed. Second, by directly including the whole TPB framework into the choice model, the decision-making mechanism underlying commute mode choice is explicitly reflected, which significantly varies with respect to specific mode choice in each segment. Either “a habitual and automatic behavior” or “a deliberate and rational decision” is finally determined. This study provides an empirical support to the moderating role of mode-use habit in the commute mode choice process, with a particular focus on its moderating role in the effect of TPB-related factors. The findings suggest that strategies to manage transport modes ownership and usage must be targeted towards specific population groups in order to gain effectiveness.

Optimised HEVC encoder intra‐only configuration

High-efficiency video coding (HEVC) is the latest video coding standard aimed to reduce the bitrate by half for the same video quality compared to H.264/AVC. This encoding performance makes HEVC more suitable for high-definition video applications. However, this performance is coupled with a high-computational complexity, which makes it hard to achieve real-time video encoding with a classic embedded processor. Multicore technology of programmable processors could be a very promising solution to overcome this computational complexity. Moreover, software optimisations by proposing fast algorithms for the most complex functions could also be an efficient solution to speed up the encoding process. In this context, this study presents a fast mode decision algorithm for the intra prediction module. This algorithm aims to reduce the number of intra prediction modes to be tested instead of performing a full intra mode search. Experimental results for all-Intra configuration show that the proposed fast intra mode decision allows saving up to 46.79% of the intra prediction time in average. Encoding performance in terms of video quality and bitrate is not significantly affected.

Adaptive CU split prediction and fast mode decision for 3D-HEVC texture coding based on just noticeable difference model

3D-high efficiency video coding (3D-HEVC) is the latest extension of HEVC standard. Through adopting novel coding tools, 3D-HEVC could improve the compression efficiency of multi-view texture video. However, with the great bitrate savings, 3D-HEVC brings the extreme complexity. To reduce the encoding time of texture video, a fast 3D-HEVC method based on just noticeable difference (JND) model is proposed in this paper. The JND model is introduced to analyze the coding tree block (CTB) characteristics of the texture image. Moreover, combining with adaptive coding unit (CU) split prediction and fast mode decision, the proposed scheme could skip some CTBs of texture video at the early stage without a normal coding procedure. Simulation results demonstrate that the proposed scheme can reduce 51.5% encoding runtime on average with negligible loss of subjective quality.

Strategic Decision-Making with Artificial Intelligence Implications for Decision Speed and Quality

Artificial Intelligence (AI) has infiltrated board rooms and holds promise to fundamentally alter strategic decision-making. Drawing a multiple bodies of related literature, we first offer a framework to classify the procedural modes decision makers adopt when they make strategic decisions and use this framework to derive our propositions. In this framework, decision makers facing use simplification strategies – by decomposition and by use of heuristics or choosing simpler problems – to make strategic decision. Using this framework, we propose that the use of AI in strategic decision-making has heterogeneous effects on the decision-making speed and decision quality. Specifically, in scenarios where delegation is the main mode of decision making, the use of AI to augment human decision makers leads to faster and better-quality decisions, whereas the replacement of heuristics and problem simplification strategies leads to slower and perceived lower quality decisions. Finally, we discuss the implications for future empirical research.

A novel fast intra mode decision for versatile video coding

AbstractThe latest video coding standard Versatile Video Coding (VVC) obtains superior coding efficiency compared to the High Efficiency Video Coding (HEVC), which is achieved by incorporating more effective and complex new coding tools. In this paper, we propose a novel fast intra mode decision algorithm for VVC, including following two strategies: (1) the correlation between the optimal modes of the adjacent blocks and the modes selected in the rough modes decision (RMD) process is analyzed and applied to reduce the modes in the candidate list; (2) modes in the candidate list are sorted in ascending order according to the modes’ cost calculated in the RMD process. An early termination method is proposed for terminating the optimal prediction mode decision process based on this new order early. These two strategies are incorporated into intra coding to reduce the coding complexity. Since these two strategies do not add any additional computational complexity, the proposed fast algorithm can achieve more complexity reduction. The experimental results show that the complexity reduction of the proposed algorithm is up to 44.74% compared to VVC reference software VTM2.0, and averagely 30.59% encoding time saving with 0.86% BDBR increase.

Synchronous target mode decision mechanism of electrically coupled neuron system under asymmetric excitation

Due to the universality of asymmetric coupling-excitation in neuronal system and the important role of firing pattern in neural information coding and transmission, synchronous target mode decision mechanism of electrically coupled neuron system under asymmetric excitation is studied in this paper. In the beginning, based on an asymmetric excitation system with symmetrical coupling, according to the phenomenon that Interval Spike Intervals Sequence (ISI) can coincide with the ISIs of a single neuron stimulated by a certain current, the concept of ‘virtual equivalent symmetrical stimulus current’ is put forward. Furthermore, it is also found that there is no relationship between the steady-state synchronous target mode and the coupling strength of symmetric coupled system under this situation. However, the following experiments of asymmetric coupling-excitation system show that there is a strong internal relationship between the almost complete synchronous stable firing mode and coupling strength as well as exciting currents. According to the experimental conclusion, a qualitative criterion is proposed, and then, on the basis that the system can be fully synchronized, the decision-making quantitative basis of the steady-state synchronization target mode of the given system is derived strictly. This study has important reference value for understanding the working state transition of neuronal system.

Energy Aware Dynamic Mode Decision for Cellular D2D Communications by Using Integrated Multi-Criteria Decision Making Model

Recent developments in mobile network communication increases the usage of the cellular users and their wideband data transfer. Sometimes, network operators may get overloaded because of voice and data transmission between nodes which are under the same coverage area. Cellular device-to-device communication is one of the emerging technologies to overcome current increasing demands for spectrum in cellular networks. It reduces network traffic at centralized terminal stations by enabling direct communication link between source and destination. Mode selection is one of the steps required to fix defined configuration for transmitting data between the pairs of nodes. In this article, the mode selection problem has been considered as multi-objective decision-making problem and applied classical multi-criteria decision models such as analytic hierarchy process and technique for order preference by similarity to the ideal solution models. From simulated results, combination of these two has been proven as the most promising combination for mode selection problem in cellular device-to-device communications under three different types of data services such as text, audio, and video. A dynamically mode selection process has to be carried out with multiple factors which are affecting the performance of the system using proposed model.

The Influences of Public Transport on Parking Space: A Study on Travel Choice Behaviour between Private Cars and Public Transport

For the last decade when travelling the majority of people rely on cars. To this extent, every public building has the obligation to provide parking area. In transport studies, a long-standing debate has been arguing about in what ways the role of travel mode choices contributes to the association between built environment and travel behaviour. In regard to the presence of the new MRT system in Jakarta, this paper is to review the literature for a theoretical exploration study about how people make their travel mode decision from private cars to public transport. Focusing on travel-to-work behaviour, the study identifies comfort, awareness, location, time management, cost, regulation, and pre-planned activities as attributes which influence travel mode choice. In addition, socio-demographics characteristics and pre-condition habits on using public transport describe how individuals view the attributes. This paper proposes awareness of public transport strategy for the stakeholders.

HEVC Intra Mode Selection Using Benford’s Law

In this paper, Benford’s law (BF) is analyzed and its feasibility is presented to solve the complexity issue of high-efficiency video coding (HEVC). Secondly, the intra modes selected for the current block are modeled using BF’s curve to identify the most important intra modes. Finally, the intra mode computation process is terminated when the probability of these intra modes does not follow BF’s curve. This termination results in an early mode decision algorithm that reduces the complexity of HEVC. Experimental results on HEVC’s dataset show that the proposed algorithm reduces the complexity of the HEVC by 32.77% and the increase in BD-BR (Bjontegaard delta bit rate) is only 1.62%.

Fast prediction mode selection and CU partition for HEVC intra coding

Compared with the predecessor H.264/advanced video coding, high-efficiency video coding (HEVC) is a new video coding standard with nearly double coding efficiency under the same coding quality. However, the computing complexity of HEVC increases sharply. To solve this problem, a fast algorithm for intra prediction mode selection based on mode grouping is proposed by reducing the number of modes entering rough mode decision. Moreover, the dual support vector machine is proposed to efficiently select the coding unit (CU) size, which is based on texture features of CU and sub-CUs including content complexity and direction complexity. By using the new CU size selection algorithm, the encoder can confirm the split for complex CU and terminate the split for simple CU in advance, so as to reduce the computing complexity of CU size selection. The experimental results show that by employing the two fast algorithms in intra coding, it can save 42.80% encoding time, with 0.98% increment in bit rate and 0.018 dB loss of peak-signal-to-noise ratio of luminance, compared with the reference software x265-1.7.

Smart Mode Selection Using Online Reinforcement Learning for VR Broadband Broadcasting in D2D Assisted 5G HetNets

As an emerging broadband service pattern in the 5G era, VR broadcasting needs a considerable amount of bandwidth and strict quality of service (QoS) control. The traditional eMBMS or enTV transmission mode in HetNets consisting of macro cells and small cells cannot bring about a good trade-off between broadband performance and resource utilization for VR broadcasting service. D2D multicasting applied to VR broadcasting can improve the performance of edge users and resource utilization. Motivated by the rapid development of AI techniques, this paper proposes a novel hybrid transmission mode selection based on online reinforcement learning to address this problem. Each VR broadband user can be associated by one of the three modes: macrocell broadcasting, mmWave small cell unicasting and D2D multicasting. This paper first models this intelligent mode decision process as a problem to pursue the optimal system throughput. Then, an online machine learning-based method is proposed to solve this problem, which consists of a fast D2D clustering module based on unsupervised learning and a smart mode selection module based on reinforcement learning. The simulation results verify that the WoLF-PHC and Nash Q-learning perform better than other algorithms in large-scale scenarios and small-scale scenarios, respectively. The proposed intelligent transmission mode selection can also achieve larger VR throughput than traditional broadcasting strategies with a good balance between broadband performance and resource utilization.

Optimal Torque Split Strategy of Dual-Motor Electric Vehicle Using Adaptive Nonlinear Particle Swarm Optimization

In order to exploit the potential of energy saving of dual-motor powertrain over single-motor powertrain, this paper proposes a time-efficient optimal torque split strategy for a front-and-rear-axle dual-motor electric powertrain. Firstly, a physical model of electric vehicle powertrain is established in Matlab/Simulink platform and further validated by real-vehicle experiments. Subsequently, a three-layer energy management strategy composed of demanded torque calculation layer, mode decision layer, and torque split layer is devised to enhance the total operating efficiency of two motors. Specifically, the optimal torque split strategy using adaptive nonlinear particle swarm optimization (ANLPSO) is embedded in the torque split layer. Finally, two conventional strategies (even distributed strategy and rule-based strategy) for dual-motor powertrain are considered for comparison to verify the efficacy of the proposed strategy. Tremendous results demonstrate that the dual-motor powertrain with this proposed optimal torque split strategy develops energy saving by 11.88% and 12.18% against single-motor powertrain in the NEDC and WLTP. Compared to two conventional torque split strategies, it is able to reduce the total motor loss by 12.17% and 8.1% in NEDC and 11.91% and 8.07% in WLTP, respectively, which indicates the prominent optimization performance and a great potential in realistic applications.