Situation-Aware Feedback-Predictive Control Framework for Lane-Less Dense Traffic
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916023292133376 |
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| author | Khound, Parthib |
| author_facet | Khound, Parthib |
| contents | Navigating dense, lane-less traffic remains one of the most challenging scenarios for autonomous vehicles, especially in emerging regions where road structure and driver behavior are highly unpredictable. This paper presents a hybrid control framework tailored for such environments, integrating a $360^\circ$ zone-based perception module with a dual-layer control strategy that combines classical feedback and predictive optimization. The longitudinal feedback controller computes reference speed based on braking distance and steering dynamics, while the lateral controller tracks a virtual optimal lane derived from the spatial distribution of neighboring vehicles. The predictive planner samples control inputs over a time horizon and selects the most feasible trajectory using a multi-term cost function. Simulation results across diverse one-way traffic scenarios demonstrate the framework's robustness, responsiveness, and suitability for chaotic, unstructured traffic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12590 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Situation-Aware Feedback-Predictive Control Framework for Lane-Less Dense Traffic Khound, Parthib Systems and Control Navigating dense, lane-less traffic remains one of the most challenging scenarios for autonomous vehicles, especially in emerging regions where road structure and driver behavior are highly unpredictable. This paper presents a hybrid control framework tailored for such environments, integrating a $360^\circ$ zone-based perception module with a dual-layer control strategy that combines classical feedback and predictive optimization. The longitudinal feedback controller computes reference speed based on braking distance and steering dynamics, while the lateral controller tracks a virtual optimal lane derived from the spatial distribution of neighboring vehicles. The predictive planner samples control inputs over a time horizon and selects the most feasible trajectory using a multi-term cost function. Simulation results across diverse one-way traffic scenarios demonstrate the framework's robustness, responsiveness, and suitability for chaotic, unstructured traffic. |
| title | Situation-Aware Feedback-Predictive Control Framework for Lane-Less Dense Traffic |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2604.12590 |