Shared Control for Vehicle Lane-Changing with Uncertain Driver Behaviors
URL: http://arxiv.org/abs/2510.25284v1
archive: archived pipeline: cataloged
Abstract
Lane changes are common yet challenging driving maneuvers that require continuous decision-making and dynamic interaction with surrounding vehicles. Relying solely on human drivers for lane-changing can lead to traffic disturbances due to the stochastic nature of human behavior and its variability under different task demands. Such uncertainties may significantly degrade traffic string stability, which is critical for suppressing disturbance propagation and ensuring smooth merging of the lane-changing vehicles. This paper presents a human-automation shared lane-changing control framework that preserves driver authority while allowing automated assistance to achieve stable maneuvers in the presence of driver's behavioral uncertainty. Human driving behavior is modeled as a Markov jump process with transitions driven by task difficulty, providing a tractable representation of stochastic state switching. Based on this model, we first design a nominal stabilizing controller that guarantees stochastic ${L}_2$ string stability under imperfect mode estimation. To further balance performance and automated effort, we then develop a Minimal Intervention Controller (MIC) that retains acceptable stability while limiting automation. Simulations using lane-changing data from the NGSIM dataset verify that the nominal controller reduces speed perturbations and shorten lane-changing time, while the MIC further reduces automated effort and enhances comfort but with moderate stability and efficiency loss. Validations on the TGSIM dataset with SAE Level 2 vehicles show that the MIC enables earlier lane changes than Level 2 control while preserving driver authority with a slight stability compromise. These findings highlight the potential of shared control strategies to balance stability, efficiency, and driver acceptance.
Summary
Control-systems paper proposing a human-automation shared longitudinal control framework for lane-changing maneuvers, in which driver behavior is modeled as a Markov jump process with mode transitions driven by task difficulty (TD). The authors derive a nominal stabilizing controller with stochastic L2 string-stability guarantees under imperfect mode estimation, then propose a Minimal Intervention Controller (MIC) that limits automated effort while preserving acceptable stability and driver authority. Validation uses lane-change trajectories from the NGSIM dataset and SAE Level 2 vehicle trajectories from the TGSIM I-90/94 dataset; no human-subjects experiment is reported. Driver-relevance comes from the explicit modeling of behavioral uncertainty, task-difficulty-driven mode switching, and the trade-off between automation effort and driver agency.
Key finding
Across NGSIM and TGSIM lane-change cases, the nominal controller reduces speed perturbations and shortens lane-changing time relative to human-only control, while the MIC achieves earlier lane changes than SAE Level 2 automation-only control (e.g., 5.90 s vs 7.17 s in TGSIM Case 1) at the cost of a small string-stability decrement, with an automated effort ratio r_int around 0.5 indicating that driver input is preserved.
Methodology
Numerical simulation study. Driver longitudinal behavior modeled with a mode-dependent Optimal Velocity Model (OVM) coupled to a two-state Markov jump process whose transitions are governed by a task-difficulty thresholding rule. Mode-dependent OVM parameters and Markov transition rates calibrated from lane-change trajectories in the NGSIM dataset and the TGSIM I-90/94 SAE-Level-2 dataset via forward-simulation fitting. Stochastic L2 string stability analyzed for a leader-follower-ego three-vehicle scenario; nominal stabilizing controller and Minimal Intervention Controller derived analytically and evaluated in simulation against human-only and L2 automation-only baselines.
Quality score: 5 / 5