Accurately evaluating the intelligence of autonomous path planning remains challenging, primarily due to the interdependencies among evaluation metrics and the insufficient integration of subjective and objective weighting methods. This paper proposes Game-Theoretic Kendall’s Coefficient (GTKC) weighting framework for evaluating autonomous path planning intelligence. The framework specifies a safety–efficiency–comfort metric system with observable, reproducible, and quantifiable metrics. To account for intermetric dependence, subjective weights are elicited via an improved Analytic Network Process (ANP), while objective weights are derived using the CRITIC method to capture contrast intensity and intercriteria conflict. The credibility of the subjective and objective weights is evaluated using Kendall’s coefficient and the coefficient of variation, respectively. Subsequently, based on the principle that higher credibility should receive greater weight, a game-theoretic optimization model is employed to dynamically derive optimal combination coefficients. Experimental results on three case scenarios demonstrate that the GTKC framework significantly outperforms existing weighting approaches in terms of effectiveness (achieving a lowest Mean Absolute Error (MAE) of 0.15 and a perfect Spearman’s correlation coefficient ($\overline{\rho }=1.0$) with ground-truth rankings), stability (Mean Standard Deviation (MSD) = 0.023), and ranking consistency (Kendall’s coefficient W = 0.924). These findings validate GTKC as a theoretically grounded and practically robust mechanism that explicitly models metric interdependencies and integrates expert knowledge with empirical evidence, enabling reliable and reproducible evaluation of autonomous path planning intelligence.

Aiming to address the core issue that the current intelligence evaluation for Unmanned Ground Vehicles (UGVs) overly rely on static performance metrics and lack dynamic quantitative characterization of learning internalization capability (LIC), this study proposes a dynamic evaluation framework based on time series analysis. The framework begins by constructing a multidimensional test scenario parameter system and collecting externally observable performance sequence data. It then introduces a sliding window-based slope-standard deviation collaborative analysis technique to achieve unsupervised division of learning phases, from which five core evaluation metrics are extracted to comprehensively quantify the multidimensional dynamic characteristics of LIC in terms of efficiency, stability, and overall effectiveness. Simulation experiments were carried out using UGVs equipped with three types of path-planning algorithms in low-, medium-, and high-difficulty scenarios. Results demonstrate that the proposed algorithm can effectively distinguish multi-dimensional differences in LIC among different UGVs, exhibiting strong discriminative power and interpretability. This study provides a standardized evaluation tool for UGV intelligent selection, algorithm iteration optimization, and training strategy design, and offering significant reference value for the evaluation of the learnability of autonomous driving systems.

This paper establishes a fundamental connection between the local time invariance of motion parameters and dead reckoning (DR) accuracy. This insight enables the reformulation of navigation parameter estimation as a convex optimization problem solvable through our novel Eight-Branch Pseudoinverse Gradient Descent Method (8B-PGDM). This method addresses non-cooperative positioning challenges in sparse-sensor regimes, particularly enabling real-time trajectory prediction when facing intermittent measurements (e.g., <5 Hz sampling rates) or persistent signal blockages. This method achieves an excellent estimation accuracy with only three samplings and an prediction MSE of $0.7906$, significantly better than traditional dead reckoning (DR) methods. This approach effectively mitigates the impact of data scarcity, enabling robust and accurate trajectory predictions in challenging environments.

This paper presents a comprehensive investigation into dead reckoning algorithms. The study begins with an in-depth analysis of the fundamental mathematical formulation for navigation position calculations, then introduces the concept of local invariance to refine traditional methods by examining state parameters with inherent stability. Building on this foundation, we propose an efficient iterative optimization algorithm designed specifically for real-time trajectory prediction under sparse sensor data conditions(Only three samples are required). This approach effectively mitigates the impact of data scarcity, enabling robust and accurate trajectory predictions in challenging environments. The proposed method is anticipated to play a pivotal role in following control systems, thereby significantly improving their operational reliability and performance.

The generalization capability of the decision-making modules in unmanned ground vehicles (UGVs) is critical for their safe deployment in unseen environments. Prevailing evaluation methods, which rely on aggregated performance over static benchmark sets, lack the granularity to diagnose the root causes of model failure, as they often conflate the distinct influences of scenario similarity and intrinsic difficulty. To overcome this limitation, we introduce a fine-grained, dynamic evaluation framework that deconstructs generalization along the dual axes of multi-level difficulty and similarity. First, scenario similarity is quantified through a four-layer hierarchical decomposition, with results aggregated into a composite similarity score. Test scenarios are independently classified into ten discrete difficulty levels via a consensus mechanism integrating large language models and task-specific proxy models. By constructing a three-dimensional (3D) performance landscape across similarity, difficulty, and task performance, we enable detailed behavioral diagnosis. The framework assesses robustness by analyzing performance within the high-similarity band (90–100%), while the full 3D landscape characterizes generalization under distribution shift. Seven interpretable metrics are derived to quantify distinct facets of both generalization and robustness. This initial validation focuses on the path-planning layer under full state observability, establishing a foundational proof-of-concept for the framework. It not only ranks algorithms but also reveals non-trivial behavioral patterns, such as the decoupling between in-distribution robustness and out-of-distribution generalization. It provides a reliable and interpretable foundation for evaluating the readiness of UGVs for safe deployment in unseen environments.