Jiaqi Liu

In hybrid traffic environments where human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist, achieving safe and robust decision-making for AV platooning remains a complex challenge. Existing platooning systems often struggle with dynamic formation management and adaptability, especially under complex and dynamic mixed-traffic conditions. To enhance autonomous vehicle platooning within these hybrid environments, this paper presents TriCoD, a twin-world safety-enhanced Data-Model-Knowledge Triple-Driven Cooperative Decision-making Framework. This framework integrates deep reinforcement learning (DRL) with model-driven approaches, enabling dynamic formation dissolution and reconfiguration through a safety-prioritized twin-world deduction mechanism. The DRL component augments traditional model-driven methods, enhancing both safety and operational efficiency, especially under emergency conditions. Additionally, an adaptive switching mechanism allows the system to seamlessly switch between data-driven and model-driven strategies based on real-time traffic demands, thus optimizing decision-making ability and adaptability. Simulation experiments and hardware-in-the-loop tests demonstrate that the proposed framework significantly improves safety, robustness, and flexibility.

Autonomous Vehicles (AVs) have entered the stage of commercialization, yet their performance in interactive scenarios remains unsatisfactory due to challenges such as decision interpretability, human driver (HV) heterogeneity, and scenario diversity. Recent advances in Large Language Models (LLMs) provide a promising avenue to enhance AV interaction capabilities, but their high computational demand hinders practical deployment. To address these challenges, this paper introduces a parallel Actor–Reasoner framework designed to enable explicit and real-time bidirectional AV-HV interactions. First, the Reasoner employs a localized LLM with CoT reasoning and human instructions to progressively infer HV intent, style, AV action, and eHMI displays during the training stage. During testing, it continues to infer he above information except AV action. The Actor, in turn, is constructed as an interaction memory through the Reasoner’s interactions with heterogeneous simulated HVs across diverse scenarios, where the memory partition and two-layer retrieval modules are employed in the construction process. During testing, the Actor is used to retrieve feasible actions for the AV. Ablation studies across multiple scenarios demonstrate that the proposed modules improve interaction success rates by an average of 15% and 12%, respectively. Moreover, comparison studies in multi-vehicle scenarios further show that the proposed Actor–Reasoner framework achieves superior safety while simultaneously improving efficiency. Finally, with the integration of external Human–Machine Interface (eHMI) information derived from the Reasoner’s reasoning and feasible actions retrieved from the Actor, the framework is validated in real-world field interactions. Our code is available at https://github.com/FanGShiYuu/Actor-Reasoner

Automated discovery of physical laws from observational data in the real world is a grand challenge in AI. Current methods, relying on symbolic regression or LLMs, are limited to uni-modal data and overlook the rich, visual phenomenological representations of motion that are indispensable to physicists. This "sensory deprivation" severely weakens their ability to interpret the inherent spatio-temporal patterns within dynamic phenomena. To address this gap, we propose VIPER-R1, a multimodal model that performs Visual Induction for Physics-based Equation Reasoning to discover fundamental symbolic formulas. It integrates visual perception, trajectory data, and symbolic reasoning to emulate the scientific discovery process. The model is trained via a curriculum of Motion Structure Induction (MSI), using supervised fine-tuning to interpret kinematic phase portraits and to construct hypotheses guided by a Causal Chain of Thought (C-CoT), followed by Reward-Guided Symbolic Calibration (RGSC) to refine the formula structure with reinforcement learning. During inference, the trained VIPER-R1 acts as an agent: it first posits a high-confidence symbolic ansatz, then proactively invokes an external symbolic regression tool to perform Symbolic Residual Realignment (SR^2). This final step, analogous to a physicist's perturbation analysis, reconciles the theoretical model with empirical data. To support this research, we introduce PhysSymbol, a new 5,000-instance multimodal corpus. Experiments show that VIPER-R1 consistently outperforms state-of-the-art VLM baselines in accuracy and interpretability, enabling more precise discovery of physical laws. Project page: https://jiaaqiliu.github.io/VIPER-R1/

Scientific testing techniques are essential for ensuring the safe operation of autonomous vehicles (AVs), with high-risk, highly interactive scenarios being a primary focus. To address the limitations of existing testing methods, such as their heavy reliance on high-quality test data, weak interaction capabilities, and low adversarial robustness, this paper proposes ExamPPO, an interactive adversarial testing framework that enables scenario-adaptive and intensity-controllable evaluation of autonomous vehicles. The framework models the Surrounding Vehicle (SV) as an intelligent examiner, equipped with a multi-head attention-enhanced policy network, enabling context-sensitive and sustained behavioral interventions. A scalar confrontation factor is introduced to modulate the intensity of adversarial behaviors, allowing continuous, fine-grained adjustment of test difficulty. Coupled with structured evaluation metrics, ExamPPO systematically probes AV's robustness across diverse scenarios and strategies. Extensive experiments across multiple scenarios and AV strategies demonstrate that ExamPPO can effectively modulate adversarial behavior, expose decision-making weaknesses in tested AVs, and generalize across heterogeneous environments, thereby offering a unified and reproducible solution for evaluating the safety and intelligence of autonomous decision-making systems.

Connected Autonomous Vehicles (CAVs) are being tested globally, but their performance in complex scenarios remains suboptimal. While cooperative driving improves CAV performance by leveraging vehicle collaboration, its lack of interaction and continuous learning limits current applications to single scenarios and specific Cooperative Driving Automation (CDA). To address these issues, this paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework for all-scenario and all-CDA applications. First, an environment module updates vehicle positions based on semantic decisions, mitigating errors from LLM-controlled positioning. Second, leveraging the four CDA levels defined in SAE J3216, a centralized-distributed coupled architecture reasoning module is developed to ensure safe and efficient cooperation through centralized negotiation and distributed decision. Finally, by introducing a memory module that employs Retrieval Augmented Generation (RAG), CAVs are endowed with the ability to learn from their past experiences to avoid repeating mistakes. Through ablation studies and comparisons with other cooperative driving methods, the results demonstrate that the proposed CoDrivingLLM significantly enhances safety, efficiency, and adaptability across various scenarios. Our code is available at: https://github.com/FanGShiYuu/CoDrivingLLM.

The cooperative driving technology of Connected and Autonomous Vehicles (CAVs) is crucial for improving the efficiency and safety of transportation systems. Learning-based methods, such as Multi-Agent Reinforcement Learning (MARL), have demonstrated strong capabilities in cooperative decision-making tasks. However, existing MARL approaches still face challenges in terms of learning efficiency and performance. In recent years, Large Language Models (LLMs) have rapidly advanced and shown remarkable abilities in various sequential decision-making tasks. To enhance the learning capabilities of cooperative agents while ensuring decision-making efficiency and cost-effectiveness, we propose LDPD, a language-driven policy distillation method for guiding MARL exploration. In this framework, a teacher agent based on LLM trains smaller student agents to achieve cooperative decision-making through its own decision-making demonstrations. The teacher agent enhances the observation information of CAVs and utilizes LLMs to perform complex cooperative decision-making reasoning, which also leverages carefully designed decision-making tools to achieve expert-level decisions, providing high-quality teaching experiences. The student agent then refines the teacher's prior knowledge into its own model through gradient policy updates. The experiments demonstrate that the students can rapidly improve their capabilities with minimal guidance from the teacher and eventually surpass the teacher's performance. Extensive experiments show that our approach demonstrates better performance and learning efficiency compared to baseline methods.

The development of autonomous vehicles has shown great potential to enhance the efficiency and safety of transportation systems. However, the decision-making issue in complex human-machine mixed traffic scenarios, such as unsignalized intersections, remains a challenge for autonomous vehicles. While reinforcement learning (RL) has been used to solve complex decision-making problems, existing RL methods still have limitations in dealing with cooperative decision-making of multiple connected autonomous vehicles (CAVs), ensuring safety during exploration, and simulating realistic human driver behaviors. In this paper, a novel and efficient algorithm, Multi-Agent Game-prior Attention Deep Deterministic Policy Gradient (MA-GA-DDPG), is proposed to address these limitations. Our proposed algorithm formulates the decision-making problem of CAVs at unsignalized intersections as a decentralized multi-agent reinforcement learning problem and incorporates an attention mechanism to capture interaction dependencies between ego CAV and other agents. The attention weights between the ego vehicle and other agents are then used to screen interaction objects and obtain prior hierarchical game relations, based on which a safety inspector module is designed to improve the traffic safety. Furthermore, both simulation and hardware-in-the-loop experiments were conducted, demonstrating that our method outperforms other baseline approaches in terms of driving safety, efficiency, and comfort.