With the development of the autonomous vehicle, traditional testing tools and methods for the automotive industry can no longer meet the needs of autonomous vehicle testing. Simulation test has huge technical advantages in test efficiency and test cost, etc. It is an important method for future testing and verification of autonomous vehicles, and its key technologies have become focal points for research. By investigating the current research and development progress of the autonomous vehicle platform, this paper presents and summarizes the simulation elements, analyzes the problems faced by the autonomous vehicle platform, and prospects the future work.

The generation of adversarial safety-critical scenarios is essential for rigorously evaluating autonomous driving systems, enabling the identification of vulnerabilities and enhancement of system robustness. However, existing methodologies predominantly focus on extreme, unconstrained collision scenarios in which non-player character (NPC) vehicles exhibit unrealistic adversarial behaviors toward the ego vehicle. While such scenarios serve as stress tests, their practical utility is limited due to two key factors: 1) these extreme events are statistically rare in real-world traffic and frequently involve collisions that are physically unavoidable, irrespective of the autonomous vehicle’s decision-making capabilities; and 2) NPC behaviors in these scenarios are often intentionally aggressive (e.g., deliberate rear-end collisions), resulting in liability attribution that predominantly lies with the NPCs rather than exposing meaningful system limitations. Recent efforts to enhance scenario plausibility rely extensively on large-scale real-world traffic datasets, introducing significant computational costs and scalability constraints. To overcome these limitations, we propose a three-layer relative safety region model that partitions the driving environment into zones of varying risk levels. This partitioning increases the likelihood that NPC vehicles will interact within relative safety boundary regions, thus enabling the generation of more realistic and contextually relevant adversarial scenarios without the need for extensive real-world traffic data. We introduce AuthSim, a platform that integrates this safety model with reinforcement learning (RL) to generate both authentic and effective safety-critical scenarios. AuthSim is the first comprehensive approach to address both the authenticity and effectiveness of autonomous driving test scenarios without relying on large-scale traffic data. Empirical results demonstrate that AuthSim outperforms existing methods, achieving a 5.25% improvement in average cut-in distance and a 11.94% increase in average collision interval time compared with the state-of-the-art (SOTA) results, all while maintaining superior efficiency in scenario generation. These findings highlight the potential of AuthSim to produce high-fidelity and efficient test cases for the rigorous evaluation of autonomous driving systems.

Recent advancements have underscored the exceptional analytical and situational understanding capabilities of Large Language Models (LLMs) in autonomous driving decisions. However, the inherent hallucination issues of LLMs pose significant safety concerns when utilized as standalone decision-making systems. To address these challenges, we propose the Hybrid-Driving framework, which leverages LLMs' situational comprehension and reasoning abilities alongside the specialized driving expertise embedded in knowledge graphs and driving rules, thereby enhancing the safety, robustness, and reliability of autonomous driving decisions. To articulate driving experiences clearly, we introduce the Scenario Evolution Knowledge Graph (SEKG), which integrates scenario prediction and action risk analysis in autonomous driving. By delineating observation areas and defining Time-to-Collision (TTC) levels, we effectively control the number of driving scenario nodes and ensure scenario diversity. Based on the scenario evolution relationships within the SEKG, we predict scenarios and assess associated action risks. Additionally, we implement a rule-filtering mechanism to eliminate unreasonable actions and employ prompt engineering to integrate scenario information, optional actions, and SEKG-based action risk analysis into the LLMs for decision-making. Extensive experiments demonstrate that our approach substantially improves decision success rates compared to using LLMs alone (≥37.5%), as well as surpasses the DiLu framework with LLMs and few-shot driving memory (≥7.5%), and other reinforcement learning methods (≥11%). These results validate the effectiveness of the Hybrid-Driving framework in enhancing LLM reliability for autonomous driving and advocate for its broader application of domain-specific knowledge across other fields.

Object detection in complex environments, such as challenging lighting conditions, adverse weather, and target occlusions, poses significant difficulties for existing algorithms. To address these challenges, this study introduces a collaborative solution integrating improved CycleGAN-based data augmentation and an enhanced object detection framework, AS-YOLO. The improved CycleGAN incorporates a dual self-attention mechanism and spectral normalization to enhance feature capture and training stability. The AS-YOLO framework integrates a channel–spatial parallel attention mechanism, an AFPN structure for improved feature fusion, and the Inner_IoU loss function for better generalization. The experimental results show that compared with YOLOv8n, mAP@0.5 and mAP@0.95 of the AS-YOLO algorithm have increased by 1.5% and 0.6%, respectively. After data augmentation and style transfer, mAP@0.5 and mAP@0.95 have increased by 14.6% and 17.8%, respectively, demonstrating the effectiveness of the proposed method in improving the performance of the model in complex scenarios.

Temporal entity alignment, the task of identifying equivalent entities across evolving knowledge graphs (KGs), is a critical yet challenging problem. Existing methods often struggle to holistically model the complex interplay between structural topology and temporal dynamics while also failing to capture long-range dependencies encoded in multi-hop relational paths. To address these limitations, we propose the Quantum-Lineage Graph Attention Network (QLGAN), a novel quantum-classical hybrid model for temporal entity alignment. QLGAN uniquely integrates a path encoder directly within its attention mechanism, which enriches neighbor representations with multi-hop relational semantics before aggregation. Furthermore, it leverages a Variational Quantum Circuit (VQC) to perform powerful non-linear feature transformations, enabling a more effective fusion of complex spatiotemporal features. The model’s alignment process is based on a hybrid similarity framework that combines quantum-inspired metrics with structurally aware temporal propagation, followed by an optimal transport alignment algorithm. Extensive experiments on benchmark datasets validate the effectiveness of our approach in learning robust and contextually-rich representations for temporal knowledge graphs.