scholarly journals A Trajectory Optimization Strategy for Connected and Automated Vehicles at Junction of Freeway and Urban Road

2021 ◽  
Vol 13 (17) ◽  
pp. 9933
Author(s):  
Zhongtai Jiang ◽  
Dexin Yu ◽  
Huxing Zhou ◽  
Siliang Luan ◽  
Xue Xing
Keyword(s):  
Trajectory Optimization ◽  
Minimum Principle ◽  
Optimization Strategy ◽  
Automated Vehicles ◽  
Simulation Experiments ◽  
Urban Road ◽  
Second Stage ◽  
Stop And Go ◽  
Vehicle Trajectory ◽  
Junction Structure

The phenomenon of stop-and-go traffic and its environmental impact has become a crucial issue that needs to be tackled, in terms of the junctions between freeway and urban road networks, which consist of freeway off-ramps, downstream intersections, and the junction section. The development of Connected and Automated Vehicles (CAVs) has provided promising solutions to tackle the difficulties that arise along intersections and freeway off-ramps separately. However, several problems still exist that need to be handled in terms of junction structure, including vehicle merging trajectory optimization, vehicle crossing trajectory optimization, and heterogeneous decision-making. In this paper, a two-stage CAV trajectory optimization strategy is presented to improve fuel economy and to reduce delays through a joint framework. The first stage considers an approach to determine travel time considering the different topological structures of each subarea to ensure maximum capacity. In the second stage, Pontryagin’s Minimum Principle (PMP) is employed to construct Hamiltonian equations to smooth vehicle trajectory under the requirements of vehicle dynamics and safety. Targeted methods are devised to avoid driving backwards and to ensure an optimal vehicle gap, which make up for the shortcomings of the PMP theory. Finally, simulation experiments are designed to verify the effectiveness of the proposed strategy. The evaluation results show that our strategy could effectively militate travel delays and fuel consumption.

Real-Time Velocity Optimization for Energy-Efficient Control of Connected and Automated Vehicles

2021 ◽  
Author(s):  
Shiying Dong ◽  
Bing Zhao Gao ◽  
Hong Chen ◽  
Yanjun Huang ◽  
Qifang Liu
Keyword(s):  
Real Time ◽  
Numerical Algorithm ◽  
Energy Efficient ◽  
Trajectory Optimization ◽  
Minimum Principle ◽  
Optimal Solution ◽  
Automated Vehicles ◽  
Pontryagin Minimum Principle ◽  
Optimal Inputs ◽  
Efficient Control

Abstract This paper presents a fast numerical algorithm for velocity optimization based on the Pontryagin' minimum principle (PMP). Considering the difficulties in the application of the PMP when state constraints exist, the penalty function approach is proposed to convert the state-constrained problem into an unconstrained one. Then this paper proposes an iterative numerical algorithm by using the explicit solution to find the optimal solution. The proposed numerical algorithm is applied to the velocity trajectory optimization for energy-efficient control of connected and automated vehicles (CAVs). Simulation results indicate that the algorithm can generate the optimal inputs in milliseconds, and a significant improvement in computational efficiency compared with traditional methods (a few seconds). Hardware in the Loop test for experimental validation is given to further verify the real-time performance of the proposed algorithm.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

The Lord of the Ring Road: A Review and Evaluation of Autonomous Control Policies for Traffic in a Ring Road

2022 ◽  
Vol 6 (1) ◽  
pp. 1-25
Author(s):  
Fang-Chieh Chou ◽  
Alben Rome Bagabaldo ◽  
Alexandre M. Bayen
Keyword(s):  
Reinforcement Learning ◽  
Traffic Control ◽  
Road Traffic ◽  
Control Algorithms ◽  
Dynamical Models ◽  
Vehicle Miles Traveled ◽  
Simulation Experiments ◽  
Traffic Condition ◽  
Ring Road ◽  
Stop And Go

This study focuses on the comprehensive investigation of stop-and-go waves appearing in closed-circuit ring road traffic wherein we evaluate various longitudinal dynamical models for vehicles. It is known that the behavior of human-driven vehicles, with other traffic elements such as density held constant, could stimulate stop-and-go waves, which do not dissipate on the circuit ring road. Stop-and-go waves can be dissipated by adding automated vehicles (AVs) to the ring. Thorough investigations of the performance of AV longitudinal control algorithms were carried out in Flow, which is an integrated platform for reinforcement learning on traffic control. Ten AV algorithms presented in the literature are evaluated. For each AV algorithm, experiments are carried out by varying distributions and penetration rates of AVs. Two different distributions of AVs are studied. For the first distribution scenario, AVs are placed consecutively. Penetration rates are varied from 1 AV (5%) to all AVs (100%). For the second distribution scenario, AVs are placed with even distribution of human-driven vehicles in between any two AVs. In this scenario, penetration rates are varied from 2 AVs (10%) to 11 AVs (50%). Multiple runs (10 runs) are simulated to average out the randomness in the results. From more than 3,000 simulation experiments, we investigated how AV algorithms perform differently with varying distributions and penetration rates while all AV algorithms remained fixed under all distributions and penetration rates. Time to stabilize, maximum headway, vehicle miles traveled, and fuel economy are used to evaluate their performance. Using these metrics, we find that the traffic condition improvement is not necessarily dependent on the distribution for most of the AV controllers, particularly when no cooperation among AVs is considered. Traffic condition is generally improved with a higher AV penetration rate with only one of the AV algorithms showing a contrary trend. Among all AV algorithms in this study, the reinforcement learning controller shows the most consistent improvement under all distributions and penetration rates.

High-Speed Reentry Vehicle Trajectory Optimization and Guidance with Lateral Maneuver

2021 ◽  
pp. 4151-4166
Author(s):  
Xiangyang Hui ◽  
Fenghua Chi ◽  
Zheng Qi ◽  
Meng Wu ◽  
Fei Li
Keyword(s):  
Trajectory Optimization ◽  
High Speed ◽  
Reentry Vehicle ◽  
Vehicle Trajectory

scholarly journals Effects of static bottlenecks on traffic flow in urban road network

2020 ◽  
Vol 12 (3) ◽  
pp. 1-15
Author(s):  
John N.P. Mahona ◽  
Cuthbert F. Mhilu ◽  
Joseph Kihedu ◽  
Hannibal Bwire
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Traffic Density ◽  
Urban Road Network ◽  
Peak Period ◽  
Flow Models ◽  
Urban Road ◽  
Stop And Go ◽  
Traffic Flow Models ◽  
Methodological Aspects

Existing traffic flow models do not consider the effects of road static bottlenecks on traffic flow. In this paper, a modified macroscopic continuum  model for traffic flow on urban road network with static bottlenecks is presented. The model takes into account the fluctuations of traffic flow considering static bottlenecks during the morning peak period. The model results show that existence of static road bottlenecks with various configurations cause traffic flow instabilities. This phenomenon lead into stop-and-go traffic flow conditions under the moderate density and reduction of the traffic system’s efficiency. Furthermore, results show that an increase in traffic density is accompanied by a significant decrease of speed which adversely influences performance of roadway and decrease the traffic system’s efficiency and thus resulting to the occurrence of congestions. The methodological aspects of the study and results will enable traffic engineers and planners to assess and improve existing urbanroad networks. Keywords: Traffic flow, Bottlenecks, stability, Stop-and-go traffic, System’s efficiency, Congestion.

Modeling and Control Using Connected and Automated Vehicles with Chained Asymmetric Driver Behavior under Stop-and-Go Oscillations

2020 ◽  
pp. 036119812096249
Author(s):  
Anupam Srivastava ◽  
Danjue Chen ◽  
Soyoung Ahn
Keyword(s):  
Order Effect ◽  
Behavior Model ◽  
Automated Vehicles ◽  
Empirical Observation ◽  
Modeling And Control ◽  
Car Following ◽  
Asymmetric Behavior ◽  
Stop And Go ◽  
Car Following Model ◽  
And Control

This paper presents a behavioral car following model, named the chained asymmetric behavior model, that improves on the asymmetric behavior model. This model is inspired by the empirical observation that vehicles react proportionately to the magnitude of disturbance experienced when traversing through a stop-and-go oscillation, deviating from a constant following behavior observed in equilibrium conditions. Findings from simulation experiments suggest that this “second-order” effect significantly affects traffic throughput and evolution under disturbances. Knowledge obtained from the model is leveraged toward designing control for connected automated vehicles in mixed traffic streams.

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