• Modular legal-interpretation layer with hybrid vector–graph RAG for AV guidance. • Two datasets: SFT and RAG for multilingual, cross-jurisdiction evaluation. • Hybrid retrieval improves faithfulness and reduces hallucination vs single modes. • Trust module scores context, groundedness, and answer relevance online. • Validated in smart-cabin, V2X intersection monitoring, and offline auditing. Autonomous vehicles (AVs) face persistent challenges in complying with complex and evolving traffic laws. Existing approaches, including rule-based, learning-based, and large language model (LLM) methods, each face limits in adaptability, generalizability, or trustworthiness. We present DriveLegal , a modular legal-interpretation framework for downstream autonomous driving applications. DriveLegal pairs fine-tuned multilingual large language models (LLMs) with an intelligent hybrid retrieval module that routes between vector search and knowledge graph, then returns concise, cited answers. A trust layer scores context relevance, groundedness, and answer relevance and supports continuous improvement through periodic automatic signals and targeted human review. We introduce the DriveLegal datasets for supervised fine-tuning and for retrieval and graph reasoning. Across benchmarks and case studies in smart cabin and vehicle-to-everything (V2X) settings, the hybrid retrieval strategy improves contextual accuracy and reduces hallucination while producing jurisdiction-aware outputs suitable for compliance checks, incident analysis, and reporting.

One of the primary challenges in urban autonomous vehicle decision-making and planning lies in effectively managing intricate interactions with diverse traffic participants characterized by unpredictable movement patterns. Additionally, interpreting and adhering to traffic regulations within rapidly evolving traffic scenarios pose significant hurdles. This paper proposed a rule-based autonomous vehicle decision-making and planning framework which extracts right-of-way from traffic rules to generate behavioural parameters, integrating them to effectively adhere to and navigate through traffic regulations. The framework considers the strong interaction between traffic participants mathematically by formulating the decision-making and planning problem into a differential game. By finding the Nash equilibrium of the problem, the autonomous vehicle is able to find optimal decisions. The proposed framework was tested under simulation as well as full-size vehicle platform, the results show that the ego vehicle is able to safely interact with surrounding traffic participants while adhering to traffic rules.

Connected autonomous vehicle (CAV) systems could bring considerable benefits to our daily lives, and possibly outperform single autonomous vehicle (AV). Nevertheless, the real-time determination of the optimal route for each connected autonomous vehicle (CAV) within a continuous space presents a considerable challenge. This difficulty arises from the exponential growth of potential motion combinations for CAVs, considering the diverse road geometries they encounter. This article proposed a CAV group planning framework to overcome this challenge. The framework works hierarchically. The global and local controllers play a crucial role in generating long-term reference paths for each CAV by employing a versatile road geometry model capable of accommodating diverse road shapes. Initially, waypoints are extracted utilizing this generalized road geometric model. Subsequently, potential combinations of waypoints are generated by considering the CAV group as a fleet. Finally, optimal waypoint combinations are assigned to each CAV by considering the CAVs’ own benefit and road usage. Reference paths for each CAV are generated using the selected waypoints and are passed on to the CAVs and roadside units (RSUs) layer. The CAVs and RSUs generate short-term motion, given the reference paths. This is operated in the Frenet frame, and the optimal motion for each CAV is selected in the aspect of the entire CAV fleet. The proposed framework is tested in simulation and has shown the ability to generate safe and sound paths under various road geometries with obstacles and in mixed traffics in real time.

Autonomous techniques are becoming increasingly integrated into our daily lives. Many advanced driver assistance systems (ADAS), including functions like lane-keeping assist and car following, are already implemented in vehicles for controlled environments such as highways. However, to enhance the capabilities of current ADAS, it is essential to extend their application to more general scenarios, like urban driving. Urban environments pose considerable challenges due to the high density of traffic participants, including pedestrians and cyclists, whose behaviors are unpredictable and necessitate strong interactions with self-driving vehicles. Addressing these complex interactions through real-time decision-making is particularly challenging but crucial for effective operation in real-world urban settings. This paper aims to bring the decision-making process of autonomous driving techniques closer to real life by proposing a motion planning and decision-making framework that utilizes game theory to formulate and consider strong interactions. Additionally, we introduce a human-like attention-based traffic actor filter to enable the autonomous vehicle to focus on critical traffic participants with a higher risk of collision. The framework is tested in both simulation and real-world scenarios, demonstrating that the algorithm can make safe and efficient decisions under various traffic scenarios involving multiple types of traffic participants in real time.

Left turning for autonomous vehicles at intersections is challenging due to the various driving behaviors from different human drivers and the strong interaction between the autonomous vehicle and human traffic participants. This paper proposes a planning and decision making framework for intersection left-turning which considers the interaction between autonomous vehicles and human drivers as well as pedestrians to address this issue. The proposed framework considers interactions mathematically by formulating the problem as a linear quadratic differential game. Through solving the Nash equilibrium of the game, the autonomous vehicle is able to properly interact with surrounding traffic participants. Under the differential game framework, the accuracy of the interaction formulation is closely related to the behavior model of human drivers. Therefore, real-world human behavior is extracted and evaluated from naturalistic driving dataset to help establish more realistic modeling and estimation of various kinds of traffic participants, including aggressive, neutral and conservative traffic participants. The simulation results show that the autonomous vehicle is able to properly estimate the types of traffic participants by observing their behavior using the proposed technique. Then the autonomous vehicle behave according to the types of those traffic participants to enable interactive and human-like planning and decision making at intersections.

Reinforcement learning (RL) has been widely used in the decision-making of autonomous vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal policy by maximizing the expectation of future returns, which lacks distributional treatments of risky situations. Additionally, various uncertainties arising from the environment could also cause unreliable decisions, particularly in some complex urban environments. In this paper, the fully parameterized quantile network (FPQN) is utilized to estimate the full return distribution. Then, the conditional value-at-risk (CVaR) is utilized with the return distribution information to generate uncertainty-aware driving behavior. Additionally, an uncontrolled four-way intersection is developed by the Simulation of Urban Mobility (SUMO) simulation platform, which considers both the surrounding vehicles (SVs) and pedestrians. More specifically, to simulate the real-world traffic environment, the uncertainty arising from the occlusion, and the behavior uncertainty of surrounding traffic participants are also considered. The experiment results suggest that the proposed method outperforms the baseline methods in terms of safety. Furthermore, the results also indicate that the proposed method can make reasonable decisions in some challenging driving cases in the presence of uncertainty.

The intersection is a critical traffic problem from the perspective of safety and traffic efficiency. As wireless communication technology advances, vehicle infrastructure cooperative approaches have received increased attention. In this paper, we propose a cooperative critical turning point method to help the cooperation between vehicles and infrastructures to improve the traffic efficiencies. The idea of cooperative critical turning point improves the cooperation between connected automated vehicles, the surrounding traffic and roadside infrastructures in order to provide high efficiencies of the intersection. An intersection management system using such a method is implemented based on the framework of the connected automated vehicle highway system. Such system can efficiently allow a roadside infrastructure receives state information from vehicles, reserve the associated intersection time-space occupancy, and then provide decision-making and planning feedback to the vehicles. The vehicles covered by the system then adjust their trajectories to meet their assigned time slot. The study validates the proposed system that considers the uncertainties of the driving environment by formulating the problem into a POMDP problem and solves it using an online solver. Based on preliminary simulation experiments, the proposed strategy can significantly reduce travel delays, decrease stops and improve the sustainability of the traffic system.