arXiv subjects

Haoxuan Dong

Publications and source records attributed to Haoxuan Dong.

6 recordsLinked to original sources

arXiv2026

Direct Data-driven Predictive Control: A Computationally Efficient Alternative to DeePC for Eco-driving in Mixed Traffic Flows

Improving energy efficiency in the transportation sector is critical for achieving sustainable mobility, with eco-driving emerging as a key strategy. However, implementing effective eco-driving for connected and automated vehicles (CAVs) in mixed traffic presents a significant control challenge due to the heterogeneous, uncertain behavior of human-driven vehicles (HDVs). Data-enabled Predictive Control (DeePC) offers a promising model-free approach but is often hindered by a high computational burden, limiting its real-time feasibility. This paper introduces a novel Direct Data-driven Predictive Control (D3PC) framework to address this limitation. By reformulating the data-driven prediction mechanism, the D3PC significantly reduces computational complexity, making its computation time nearly invariant to historical data size. This computational efficiency directly enables the formulation of a sophisticated eco-driving controller that can solve the complex energy optimization problem in real time, even within diverse and stochastic mixed-traffic environments. Comprehensive simulations demonstrate that the D3PC is orders of magnitude faster than existing DeePC-based methods while achieving superior energy efficiency. Specifically, it reduces total platoon energy consumption by up to 10.71% compared to rule-based cruise control baselines and 3.80% compared to the original DeePC, confirming its effectiveness for real-time, energy-efficient control.

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arXiv2026

Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency

Lane changing entails simultaneous longitudinal and lateral motions that affect driving comfort and mobility efficiency. Because these motions are tightly coupled and subject to substantial inter-vehicle variability, trajectory planning for lane-change maneuvers is characterized by a highly personalized nature. This study proposes a neural network-driven planner that integrates a third-order polynomial trajectory generator with a learning module that infers optimal trajectory parameters across diverse driving conditions. Using a shared backbone with dual heads, one head ensures all-condition operational guarantees, while the other captures driver-specific preferences for comfort or mobility efficiency. A head-gated switching mechanism, realized through a statistical gate based on error-winner logistic regression, adaptively selects the appropriate head under varying driving conditions, which enables context-aware lane-change trajectory planning. Representative cases and Monte Carlo simulations show that the proposed planner achieves personalized comfort and mobility during lane changes, while the baseline ensures feasible trajectories under driving conditions where personalized data are insufficient or inaccessible.

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arXiv2024

Integrated Power and Thermal Management for Enhancing Energy Efficiency and Battery Life in Connected and Automated Electric Vehicles

Effective power and thermal management are essential for ensuring battery efficiency, safety, and longevity in Connected and Automated Electric Vehicles (CAEVs). However, real-time implementation is challenging due to the multi-timescale dynamics and complex trade-offs between energy consumption, battery degradation, traffic efficiency, and thermal regulation. This paper proposes a novel integrated power and thermal management strategy based on the Multi-Horizon Model Predictive Control (MH-MPC) framework to enhance energy efficiency, optimize battery temperature, ensure traffic safety and efficiency, and reduce battery degradation for CAEVs. The proposed strategy is formulated with a focus on the aging term, allowing it to more effectively manage the trade-offs between energy consumption, battery degradation, and temperature regulation. Moreover, the proposed strategy leverages multi-horizon optimization to achieve substantial improvements, reducing computation time by 7.18%, cooling energy by 14.22%, traction energy by 8.26%, battery degradation loss by over 22%, and battery degradation inconsistency by 36.57% compared to the benchmark strategy. Furthermore, sensitivity analyses of key parameters, including weighting factors, sampling time, and prediction horizons, demonstrate the robustness of the strategy and underscore its potential for practical applications in extending battery lifespan while ensuring safety and efficiency.

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arXiv2024

Physics-Augmented Data-EnablEd Predictive Control for Eco-driving of Mixed Traffic Considering Diverse Human Behaviors

Data-driven cooperative control of connected and automated vehicles (CAVs) has gained extensive research interest as it can utilize collected data to generate control actions without relying on parametric system models that are generally challenging to obtain. Existing methods mainly focused on improving traffic safety and stability, while less emphasis has been placed on energy efficiency in the presence of uncertainties and diversities of human-driven vehicles (HDVs). In this paper, we employ a data-enabled predictive control (DeePC) scheme to address the eco-driving of mixed traffic flows with diverse behaviors of human drivers. Specifically, by incorporating the physical relationship of the studied system and the Hankel matrix update from the generalized behavior representation to a particular one, we develop a new Physics-Augmented Data-EnablEd Predictive Control (PA-DeePC) approach to handle human driver diversities. In particular, a power consumption term is added to the DeePC cost function to reduce the holistic energy consumption of both CAVs and HDVs. Simulation results demonstrate the effectiveness of our approach in accurately capturing random human driver behaviors and addressing the complex dynamics of mixed traffic flows, while ensuring driving safety and traffic efficiency. Furthermore, the proposed optimization framework achieves substantial reductions in energy consumption, i.e., average reductions of 4.83% and 9.16% when compared to the benchmark algorithms.

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arXiv2023

Overtaking-enabled Eco-approach Control at Signalized Intersections for Connected and Automated Vehicles

Preceding vehicles typically dominate the movement of following vehicles in traffic systems, thereby significantly influencing the efficacy of eco-driving control that concentrates on vehicle speed optimization. To potentially mitigate the negative effect of preceding vehicles on eco-driving control at the signalized intersection, this paper proposes an overtakingenabled eco-approach control (OEAC) strategy. It combines driving lane planning and speed optimization for connected and automated vehicles to relax the first-in-first-out queuing policy at the signalized intersection, minimizing the target vehicle's energy consumption and travel delay. The OEAC adopts a receding horizon two-stage control framework to derive optimal driving trajectories for adapting to dynamic traffic conditions. In the first stage, the driving lane optimization problem is formulated as a Markov decision process and solved using dynamic programming, which takes into account the uncertain disturbance from preceding vehicles. In the second stage, the vehicle's speed trajectory with the minimal driving cost is optimized rapidly using Pontryagin's minimum principle to obtain the closed-form analytical optimal solution. Extensive simulations are conducted to evaluate the effectiveness of the OEAC. The results show that the OEAC is excellent in driving cost reduction over constant speed and regular eco-approach and departure strategies in various traffic scenarios, with an average improvement of 20.91% and 5.62%, respectively.

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arXiv2022

Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity

This paper presents an adaptive leading cruise control strategy for the connected and automated vehicle (CAV) and first considers its impact on the following human-driven vehicle (HDV) with diverse driving characteristics in the unified optimization framework for improved holistic energy efficiency. The car-following behaviors of HDV are statistically calibrated using the Next Generation Simulation dataset. In a typical single-lane car-following scenario where CAVs and HDVs share the road, the longitudinal speed control of CAVs can substantially reduce the energy consumption of the following HDV by avoiding unnecessary acceleration and braking. Moreover, apart from the objectives including car-following safety and traffic efficiency, the energy efficiencies of both CAV and HDV are incorporated into the reward function of reinforcement learning. The specific driving pattern of the following HDV is learned in real-time from historical speed information to predict its acceleration and power consumption in the optimization horizon. A comprehensive simulation is conducted to statistically verify the positive impacts of CAV on the holistic energy efficiency of the mixed traffic flow with uncertain and diverse human driving behaviors. Simulation results indicate that the holistic energy efficiency is improved by 4.38% on average.

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