Scheduling-Based Optimization for Motion Coordination of Autonomous Vehicles at Multilane Intersections

This paper considers the motion coordination problem of autonomous vehicles in an intersection of a traffic network. The featured challenge is the design of an intersection traffic manager, in the form of a supervisory control algorithm, that regulates the motion of the autonomous vehicles in the intersection. We cast the multivehicle coordination task as an optimization problem, with a one-dimensional search-space. A model- and optimization-based heuristic method is employed to compute the control policy that results in the collision-free motion of the vehicles at the intersection and, at the same time, minimizes their delay. Our approach depends on a computation framework that makes the need for complex analytical derivations obsolete. A complete account of the computational complexity of the algorithm, parameterized by the configuration parameters of the problem, is provided. Extensive numerical simulations validate the applicability and performance of the proposed autonomous intersection traffic manager.

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Social behavior for autonomous vehicles

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1820676116 ◽

2019 ◽

Vol 116 (50) ◽

pp. 24972-24978 ◽

Cited By ~ 13

Author(s):

Wilko Schwarting ◽

Alyssa Pierson ◽

Javier Alonso-Mora ◽

Sertac Karaman ◽

Daniela Rus

Keyword(s):

Social Behavior ◽

Autonomous Vehicles ◽

Social Preferences ◽

Autonomous Vehicle ◽

Dynamic Game ◽

Control Policy ◽

Social Value Orientation ◽

Best Response ◽

The Social ◽

And Performance

Deployment of autonomous vehicles on public roads promises increased efficiency and safety. It requires understanding the intent of human drivers and adapting to their driving styles. Autonomous vehicles must also behave in safe and predictable ways without requiring explicit communication. We integrate tools from social psychology into autonomous-vehicle decision making to quantify and predict the social behavior of other drivers and to behave in a socially compliant way. A key component is Social Value Orientation (SVO), which quantifies the degree of an agent’s selfishness or altruism, allowing us to better predict how the agent will interact and cooperate with others. We model interactions between agents as a best-response game wherein each agent negotiates to maximize their own utility. We solve the dynamic game by finding the Nash equilibrium, yielding an online method of predicting multiagent interactions given their SVOs. This approach allows autonomous vehicles to observe human drivers, estimate their SVOs, and generate an autonomous control policy in real time. We demonstrate the capabilities and performance of our algorithm in challenging traffic scenarios: merging lanes and unprotected left turns. We validate our results in simulation and on human driving data from the NGSIM dataset. Our results illustrate how the algorithm’s behavior adapts to social preferences of other drivers. By incorporating SVO, we improve autonomous performance and reduce errors in human trajectory predictions by 25%.

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Ergodic control of diffusions with random intervention times

Journal of Applied Probability ◽

10.1017/jpr.2020.80 ◽

2021 ◽

Vol 58 (1) ◽

pp. 1-21

Author(s):

Harto Saarinen ◽

Jukka Lempa

Keyword(s):

Optimal Solution ◽

Singular Control ◽

Control Policy ◽

Control Problems ◽

Ergodic Control ◽

Linear Diffusion ◽

One Dimensional ◽

And Diffusion ◽

Independent Poisson Process ◽

Exact Amount

AbstractWe study an ergodic singular control problem with constraint of a regular one-dimensional linear diffusion. The constraint allows the agent to control the diffusion only at the jump times of an independent Poisson process. Under relatively weak assumptions, we characterize the optimal solution as an impulse-type control policy, where it is optimal to exert the exact amount of control needed to push the process to a unique threshold. Moreover, we discuss the connection of the present problem to ergodic singular control problems, and illustrate the results with different well-known cost and diffusion structures.

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Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060377 ◽

2021 ◽

Vol 10 (6) ◽

pp. 377

Author(s):

Chiao-Ling Kuo ◽

Ming-Hua Tsai

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Autonomous Vehicles ◽

Detection Accuracy ◽

Geospatial Information ◽

Combination Rules ◽

And Performance ◽

Road Characteristics ◽

Machine Readable ◽

Background Image

The importance of road characteristics has been highlighted, as road characteristics are fundamental structures established to support many transportation-relevant services. However, there is still huge room for improvement in terms of types and performance of road characteristics detection. With the advantage of geographically tiled maps with high update rates, remarkable accessibility, and increasing availability, this paper proposes a novel simple deep-learning-based approach, namely joint convolutional neural networks (CNNs) adopting adaptive squares with combination rules to detect road characteristics from roadmap tiles. The proposed joint CNNs are responsible for the foreground and background image classification and various types of road characteristics classification from previous foreground images, raising detection accuracy. The adaptive squares with combination rules help efficiently focus road characteristics, augmenting the ability to detect them and provide optimal detection results. Five types of road characteristics—crossroads, T-junctions, Y-junctions, corners, and curves—are exploited, and experimental results demonstrate successful outcomes with outstanding performance in reality. The information of exploited road characteristics with location and type is, thus, converted from human-readable to machine-readable, the results will benefit many applications like feature point reminders, road condition reports, or alert detection for users, drivers, and even autonomous vehicles. We believe this approach will also enable a new path for object detection and geospatial information extraction from valuable map tiles.

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Fuzzy-Based Thermal Management Scheme for 3D Chip Multicores with Stacked Caches

Electronics ◽

10.3390/electronics9020346 ◽

2020 ◽

Vol 9 (2) ◽

pp. 346 ◽

Cited By ~ 1

Author(s):

Lili Shen ◽

Ning Wu ◽

Gaizhen Yan

Keyword(s):

Power Consumption ◽

Thermal Management ◽

System Performance ◽

Control Policy ◽

Three Dimension ◽

Processor Core ◽

Management Scheme ◽

And Performance ◽

On Chip ◽

Silicon Vias

By using through-silicon-vias (TSV), three dimension integration technology can stack large memory on the top of cores as a last-level on-chip cache (LLC) to reduce off-chip memory access and enhance system performance. However, the integration of more on-chip caches increases chip power density, which might lead to temperature-related issues in power consumption, reliability, cooling cost, and performance. An effective thermal management scheme is required to ensure the performance and reliability of the system. In this study, a fuzzy-based thermal management scheme (FBTM) is proposed that simultaneously considers cores and stacked caches. The proposed method combines a dynamic cache reconfiguration scheme with a fuzzy-based control policy in a temperature-aware manner. The dynamic cache reconfiguration scheme determines the size of the cache for the processor core according to the application that reaches a substantial amount of power consumption savings. The fuzzy-based control policy is used to change the frequency level of the processor core based on dynamic cache reconfiguration, a process which can further improve the system performance. Experiments show that, compared with other thermal management schemes, the proposed FBTM can achieve, on average, 3 degrees of reduction in temperature and a 41% reduction of leakage energy.

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Different Reasons for Different Responses: A Review of Incumbents’ Adaptation in Carbon-Intensive Industries

Organization & Environment ◽

10.1177/1086026619893990 ◽

2020 ◽

pp. 108602661989399 ◽

Cited By ~ 2

Author(s):

Franziska Sump ◽

Sangyoon Yi

Keyword(s):

Carbon Emissions ◽

Organizational Adaptation ◽

Three Dimensions ◽

External Shocks ◽

Review Of The Literature ◽

Organizational Responses ◽

Environmental Pressure ◽

One Dimensional ◽

And Performance ◽

Performance Heterogeneity

Organizations often respond in different ways to common external shocks. To advance theories on organizational adaptation and performance heterogeneity, it is essential to understand different reasons for different organizational responses. We examine how incumbents in carbon-intensive industries adapt to heightened environmental pressure to reduce carbon emissions. Based on a review of the literature, we propose three dimensions along which diverse organizational responses can be efficiently mapped out: goal, timing, and scope. Building on our proposed dimensions, we develop a typology of five different organizational responses. With this, we show that organizational responses are more diverse than a one-dimensional scale could show but that the heterogeneity is somehow limited as the positions on the dimensions are not independent but correlated. To understand this observed limited heterogeneity, we proceed by identifying reasons behind different organizational responses. Furthermore, we discuss the theoretical implications of our findings for research on organizational adaptation and sustainability.

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Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015500177 ◽

2015 ◽

Vol 24 (05) ◽

pp. 1550017 ◽

Cited By ~ 1

Author(s):

Aderemi Oluyinka Adewumi ◽

Akugbe Martins Arasomwan

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Search Space ◽

Particle Swarm Optimizer ◽

Search Range ◽

Original Algorithm ◽

Inertia Weight ◽

And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

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Scheduling-driven Motion Coordination of Autonomous Vehicles at a Multi-Lane Traffic Intersection

2018 Annual American Control Conference (ACC) ◽

10.23919/acc.2018.8431374 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mehmet Ali Guney ◽

Ioannis A. Raptis

Keyword(s):

Autonomous Vehicles ◽

Motion Coordination ◽

Traffic Intersection

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Sine-Cosine Optimization-Based Bijective Substitution-Boxes Construction Using Enhanced Dynamics of Chaotic Map

Complexity ◽

10.1155/2018/9389065 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 25

Author(s):

Amer Awad Alzaidi ◽

Musheer Ahmad ◽

Hussam S. Ahmed ◽

Eesa Al Solami

Keyword(s):

Fitness Function ◽

Chaotic Map ◽

Search Space ◽

Initial Population ◽

Substitution Boxes ◽

Novel Method ◽

Sine Cosine Algorithm ◽

And Performance ◽

Optimization Mechanism ◽

Independence Criterion

This paper proposes a novel method of constructing strong substitution-boxes (S-boxes) of order n (4 ≤ n ≤ 8) based on a recent optimization algorithm known as sine-cosine algorithm (SCA). The paper also proposes a new 1D chaotic map, which owns enhanced dynamics compared to conventional chaotic map, for generating initial population of S-boxes and facilitating the optimization mechanism of SCA. The proposed method applies the SCA with enhanced chaotic map to explore and exploit the search space for obtaining optimized S-boxes on the basis of maximization of nonlinearity as fitness function. The S-box construction involves three phases such as initialization of population, optimization, and adjustment. The simulation and performance analyses are done using standard measures of nonlinearity, strict avalanche criterion, bits independence criterion, differential uniformity, linear approximation probability, and autocorrelation function. The obtained experimental results are compared with some immediate optimization-based and other S-boxes to show the strength of proposed method for constructing bijective S-boxes of salient cryptographic features.

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