The rapid development of advanced sensing and artificial intelligence technologies, has advanced autonomous driving (AD) systems by providing intelligent route planning decisions. However, how to construct an interpretable and efficient decision-making method that can adapt to various complex driving scenarios has become an important and challenging research topic. In this article, a knowledge graph (KG) for AD systems is constructed based on heterogeneous data such as traffic rules and network information. A deep learning model combining bidirectional long short-term memory and conditional random field is used to achieve joint learning of entity recognition and relationship extraction. In order to make decisions on driving behaviors, this article introduces a deep reinforcement learning framework designed to perform knowledge reasoning over the driving KG, which integrates an integrated reward function and an action dropout mechanism. Experimental comparisons against the other advanced knowledge reasoning algorithms on a practical driving rules dataset validate the effectiveness and advantages of the proposed method, with an overall mean average precision exceeding 94%. The validity of the proposed method has also been verified on the simulation platform in different road scenarios such as multilane, roundabout, intersection and thru-junction.

When autonomous vehicles (AVs) encounter sudden hazards, how to take the correct collision-free measures in emergencies has always been a focus of research. The reachable sets method has been widely applied in local path planning methods for AVs to address the collision-free path planning problem for dense, dynamic obstacles in traffic scenarios. However, the reachable sets method is limited by its inherent tendency to overestimate obstacles, making it difficult to quantify the probability of conflicts between other traffic participants and AVs. Furthermore, path planning within the reachable set may encounter local minima, potentially resulting in planning failure.This paper proposes a reachable probabilistic distribution (RPD) method, integrating reachable sets with the Interactive Multiple Model Filtering (IMM) method. The proposed method begins by collecting surrounding environmental data using Vehicle-to-Everything (V2X) technology. It then models both the obstacles and the ego vehicle, followed by constructing the occupancy sets of the obstacles and the reachable set of the ego vehicle. IMM then predicts obstacle behavior to determine their probabilities of being in each lane at future intervals. These probabilities are combined with obstacles’ occupancy sets and ego vehicle’s reachable sets to derive RPD. Compared to traditional reachable sets, the RPD framework leverages probabilistic information to simplify candidate path evaluation, greatly enhancing computational efficiency. To avoid local minima failures in local path planning within RPD, a method combining discrete strategies with Model Predictive Control (MPC) is proposed. The proposed method offers a safety reference for intelligent transportation systems based on V2X technology. It employs discrete strategies to achieve comprehensive coverage of feasible local paths, while MPC is used for real-time tracking. Validated in AVs, this method ensures robust collision avoidance with stability and safety, demonstrating its applicability in intelligent transportation systems.

Safety is a paramount concern in the realm of autonomous vehicles. Developing precise safety assessment is challenging due to the need to blend qualitative and quantitative analyses of various safety factors. To address this challenge, this paper presents an innovative safety assessment method based on the cloud model. This method employs fundamental cloud model elements like expectation, entropy, and ultra-entropy. It also employs a sophisticated double conditional single rule generator to integrate multiple assessment indicators, resulting in an integrated risk assessment cloud. This cloud dynamically represents varying risk levels based on indicator characteristics. The method evaluates the real-time safety level by assessing the proximity between the integrated risk assessment cloud and the standard cloud. This proximity analysis reveals the prevailing risk level. Empirical validation involves rigorous testing within typical scenarios, demonstrating the utility and potential of the method to assess safety for decision-making of autonomous vehicles. The capacity of the method to monitor and assess autonomous vehicle decision-making systems makes it a significant contribution to the field. Beyond empirical contributions, this paper offers theoretical insights that can shape the future of safety assessment methods for autonomous vehicles. In summary, this paper emphasizes the importance of safety for autonomous vehicles and paves the way for evolving safety assessment methods in this dynamic field.

Along with the promotion of intelligent connected vehicles (ICVs), the problems of network attacks have rapidly increased, and thus the cybersecurity has drawn much attention. Unfortunately, although remarkable progress has been achieved both in technics and standard, it still remains vague for designing vehicular cybersecurity. In this article, the general technical profile of cybersecurity for ICVs has been comprehensively reviewed, including threat analysis and risk assessment, static defense, and intrusion detection. The potential attacking vulnerabilities for ICVs are summarized, within in-vehicle network and mobile networks. Then, the identity authentication and secure communication methods are introduced from static defense, where the conventional and novel intelligent approach are included. And the intrusion detection is introduced as the active methods, including conventional and novel ones. Moreover, the general procedure and management for designing the vehicular cybersecurity are also summarized according to the current standard system. It hopes that the review of research progress on technical method may help researchers and manufactures, and delivers the potential direction for future cybersecurity development.

Nowadays, human-oriented has already become the direction of the development of the intelligent vehicle, among which, the cabin, in constant contact with drivers, is getting more and more attention. Intelligent assisted systems have alleviated the burden on drivers during long journeys and provided a remedy for operational errors. As the trend towards increasingly intelligent vehicles, the issue of human-machine co-driving is receiving attention from scientific researchers. The technologies of human-machine interactions usually contain two parts, the human-to-vehicle and vehicle-to-human. This paper analyzes the potential innovation of human-machine systems from the perspective of human sensing, including visual, auditory, tactile, and olfactory. Based on the review of human-machine technologies, the current intelligentization of vehicles is divided into driver interaction and crew service systems. Then, the structure of a future intelligent interaction system considering multi-sensing is proposed and further discussed. Finally, by analyzing the relationship between the system for human and autonomous systems, a classification of the intelligence level for interaction systems is presented.