Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles

Wang, Pangwei; Wang, Yunfeng; Deng, Hui; Zhang, Mingfang; Zhang, Juan

doi:10.1155/2020/8819911

Cited by 8 publications

(3 citation statements)

References 22 publications

(22 reference statements)

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“…Studies have been performed on the topic but typically relate to delay or vehicular emissions [ 7 , 9 , 10 , 11 ] and understate the importance of the trajectory optimization process as it relates to safety. Furthermore, these studies rarely consider computational complexity and rely on techniques such as mixed-integer linear programming or dynamic programming approaches [ 7 , 42 ]. To the best of the author’s knowledge, this is the first study that seeks to optimize connected vehicle trajectories at intersections using speed advisories with respect to safety in real-time.…”

Section: Previous Workmentioning

confidence: 99%

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Ghoul

Sayed

2021

Sensors

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Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

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Section: Previous Workmentioning

confidence: 99%

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Ghoul

Sayed

2021

Sensors

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“…Traffic signal control is a fundamental element in traffic guidance at urban signalized intersections [26,27]. The core of the integration between traffic signal control and traffic guidance is in temporal and spatial synchronization [28]. At the macro level, traffic signal control can actively guide the drivers to choose the path by combining the traffic guidance information to balance the traffic pressure.…”

Section: Data From Traffic Signal Controllermentioning

confidence: 99%

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

et al. 2021

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Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

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“…Better prediction performance is obtained by optimizing the LSTM network parameters. With the widespread application of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies [ 22 , 23 , 24 ], the accuracy of trajectory prediction under urban scenarios has been improved greatly by combining the advantages of sensor fusion technologies. Zyner et al [ 25 ] proposed a trajectory prediction method based on multimodal probabilistic solutions, which combined recurrent neural networks (RNNs) with mixture density networks (MDNs) to predict vehicle trajectories with high prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

Wang

Liu

et al. 2023

Sensors

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Intelligent connected vehicles (ICVs) have played an important role in improving the intelligence degree of transportation systems, and improving the trajectory prediction capability of ICVs is beneficial for traffic efficiency and safety. In this paper, a real-time trajectory prediction method based on vehicle-to-everything (V2X) communication is proposed for ICVs to improve the accuracy of their trajectory prediction. Firstly, this paper applies a Gaussian mixture probability hypothesis density (GM-PHD) model to construct the multidimension dataset of ICV states. Secondly, this paper adopts vehicular microscopic data with more dimensions, which is output by GM-PHD as the input of LSTM to ensure the consistency of the prediction results. Then, the signal light factor and Q-Learning algorithm were applied to improve the LSTM model, adding features in the spatial dimension to complement the temporal features used in the LSTM. When compared with the previous models, more consideration was given to the dynamic spatial environment. Finally, an intersection at Fushi Road in Shijingshan District, Beijing, was selected as the field test scenario. The final experimental results show that the GM-PHD model achieved an average error of 0.1181 m, which is a 44.05% reduction compared to the LiDAR-based model. Meanwhile, the error of the proposed model can reach 0.501 m. When compared to the social LSTM model, the prediction error was reduced by 29.43% under the average displacement error (ADE) metric. The proposed method can provide data support and an effective theoretical basis for decision systems to improve traffic safety.

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Multilane Spatiotemporal Trajectory Optimization Method (MSTTOM) for Connected Vehicles

Cited by 8 publications

References 22 publications

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

Real-Time Trajectory Prediction Method for Intelligent Connected Vehicles in Urban Intersection Scenarios

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