Dual-MPC-Driven Modeling and Spatiotemporal Evolution of Intelligent Connected Traffic Risk Fields

JIANG Linyuan; DING Fei; FAN Xuan; YANG Xuechao; SONG Aiguo; ZHANG Dengyin

doi:10.11999/JEIT260194

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JIANG Linyuan, DING Fei, FAN Xuan, YANG Xuechao, SONG Aiguo, ZHANG Dengyin. Dual-MPC-Driven Modeling and Spatiotemporal Evolution of Intelligent Connected Traffic Risk Fields[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260194

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JIANG Linyuan, DING Fei, FAN Xuan, YANG Xuechao, SONG Aiguo, ZHANG Dengyin. Dual-MPC-Driven Modeling and Spatiotemporal Evolution of Intelligent Connected Traffic Risk Fields[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260194

Citation:

JIANG Linyuan, DING Fei, FAN Xuan, YANG Xuechao, SONG Aiguo, ZHANG Dengyin. Dual-MPC-Driven Modeling and Spatiotemporal Evolution of Intelligent Connected Traffic Risk Fields[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260194

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Dual-MPC-Driven Modeling and Spatiotemporal Evolution of Intelligent Connected Traffic Risk Fields

doi: 10.11999/JEIT260194 cstr: 32379.14.JEIT260194

1.
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
School of Intelligent Transportation, Nanjing University of Posts and Telecommunications, Najing 210003, China
3.
Institute of Space Science and Technology, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China(62471241), The Major Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (25KJA510003), The “Six Talent Peaks” High Level Talent Funding Project of Jiangsu Province (DZXX-008), Jiangsu Graduate Research and Practice Innovation Program (SJCX24_0336)

Received Date: 2026-02-11
Accepted Date: 2026-04-23
Rev Recd Date: 2026-04-22

Available Online: 2026-05-13

Abstract

Abstract

Objective With the deployment of intelligent connected vehicle-road-cloud cooperative systems, roadside infrastructure is evolving from traffic-state sensing units into intelligent decision-support platforms for multi-vehicle interaction analysis and dynamic risk inference. In highway and urban freeway scenarios, traffic operation is affected not only by the kinematic responses of individual vehicles but also by lane-changing intentions, car-following competition, and conflict propagation under local interactions. From a roadside perspective, a unified framework is therefore needed to continuously represent traffic risk, reveal its spatiotemporal evolution, and couple risk information with behavior decision-making and trajectory planning. Existing car-following models, such as the Optimal Velocity Model (OVM), Full Velocity Difference (FVD) Model, and Intelligent Driver Model (IDM), can describe speed-spacing evolution. However, these models mainly focus on longitudinal interactions and usually embed risk implicitly in safety-distance or acceleration constraints. They cannot explicitly characterize the coupling between longitudinal following and lateral lane changing, nor can they provide a continuous risk representation suitable for regional traffic assessment. Although Artificial Potential Field (APF) methods and Model Predictive Control (MPC) methods can improve trajectory safety, existing studies still lack a unified mechanism that links risk assessment, behavior decision-making, and motion planning. In addition, discrete behavior choices and continuous control actions are difficult to process efficiently within a single optimization framework. Methods An intelligent connected traffic risk-field model oriented toward vehicle-road cooperation is first established. The model integrates vehicle-interaction risk, lane-marking constraint risk, and road-boundary repulsive risk (Fig. 1). In the vehicle-interaction layer, motion-state-induced risk is formulated by considering the relative speed and relative orientation between the ego vehicle and surrounding vehicles. Distance-induced risk is modeled to reflect attenuation as separation distance increases (Fig. 2(a)–(b)). To represent the stronger influence of forward hazards than lateral and rear hazards, a directional non-uniformity coefficient is used. This coefficient adjusts the angular attenuation of field strength and enables anisotropic spatial risk representation around the vehicle. In the road-constraint layer, lane markings and road boundaries are modeled separately. The total driving risk field is obtained by weighting and combining the lane-marking field, road-boundary field, and multi-vehicle interaction field (Fig. 2(e)–(f)). Based on this representation, a dual-MPC hierarchical decision and motion-planning architecture is designed (Fig. 3). In each control cycle, the upper layer evaluates candidate behavior modes according to the vehicle state, surrounding traffic state, and dynamic risk field, and then outputs a unique behavior mode. The lower layer activates the corresponding control branch. When lane keeping is selected, longitudinal speed-planning MPC is used. When lane changing is selected, lane-change trajectory-planning MPC is activated under road-boundary, lane-marking, and safe-gap constraints. Results and Discussions The proposed framework reconstructs microscopic traffic risk evolution under different datasets and time scales. In the HighD highway scenario, when the slicing interval is 0.4 s, the local evolution of following and lane-changing interactions is captured in detail. This includes the process in which the ego vehicle initially follows a preceding vehicle and then starts changing to the adjacent lane (Fig. 4(a)–(d)). When the interval is increased to 1.0 s, a wider spatiotemporal interaction range becomes visible, and lane-change completion and the subsequent return maneuver are identified more clearly (Fig. 4(e)–(h)). In the NGSIM scenario, a smaller interval provides a finer description of the lane-change disturbance process. By contrast, a larger interval reveals the wider reconstruction of interaction relationships among the original lane, target lane, and surrounding vehicles (Fig. 4(i)–(p)). These results indicate that the proposed roadside-oriented risk field can describe both local interaction details and larger-scale evolution trends, depending on the selected reconstruction interval. Sensitivity experiments further confirm the role of the directional non-uniformity coefficient. As this coefficient increases, forward risk concentration becomes stronger, local peak risk increases, and the coverage of high-risk regions decreases (Table 2). This finding shows that the coefficient effectively regulates anisotropic field distribution. Comparative experiments with IDM, OVM, FVD, and APF show that the proposed method performs better in most representative scenarios and error metrics (Fig. 5, Table 3). In the lateral cut-in scenario, its advantage lies in the early representation of lateral intrusion risk, which enables the behavior decision layer to anticipate conflict and the motion-planning layer to generate continuous evasive actions. In congested scenarios, the superposition of forward congestion risk, lateral neighboring-vehicle influence, and road-boundary constraints allows the dual-MPC controller to evaluate safety and feasibility simultaneously in local space. Conclusions A unified framework for roadside-oriented traffic-risk modeling and behavior-driven trajectory planning is developed. By integrating multi-vehicle interaction risk, lane-marking constraint risk, and road-boundary repulsive risk into a continuously evolving dynamic risk field, the spatial quantification of multi-vehicle interaction risk is realized. The directional non-uniformity coefficient further enables asymmetric risk perception modeling in forward, lateral, and rear directions. On this basis, a dual-MPC hierarchical architecture is constructed to couple behavior decision-making with motion planning, so that lane-keeping and lane-changing behaviors can be adaptively selected and optimized under a unified risk-driven mechanism. Experiments based on HighD and NGSIM datasets show that the proposed method can effectively characterize the spatiotemporal evolution of traffic-risk fields and outperform representative comparison models in most typical scenarios and error metrics.
- multi-vehicle interaction risk field,
- dual model predictive control,
- trajectory planning,
- vehicle-road cooperation,
- spatiotemporal evolution of risk

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References(24)

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