The paper presents a vision-based obstacle avoidance strategy for lightweight self-driving cars that can be run on a CPU-only device using a single RGB-D camera. The method consists of two steps: visual perception and path planning. The visual perception part uses ORBSLAM3 enhanced with optical flow to estimate the car’s poses and extract rich texture information from the scene. In the path planning phase, the proposed method employs a method combining a control Lyapunov function and control barrier function in the form of a quadratic program (CLF-CBF-QP) together with an obstacle shape reconstruction process (SRP) to plan safe and stable trajectories. To validate the performance and robustness of the proposed method, simulation experiments were conducted with a car in various complex indoor environments using the Gazebo simulation environment. The proposed method can effectively avoid obstacles in the scenes. The proposed algorithm outperforms benchmark algorithms in achieving more stable and shorter trajectories across multiple simulated scenes.

With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. Most of the existing related reviews explore the application of AI in blockchain as a whole but lack in-depth classification and discussion on how AI can empower the core aspects of blockchain. This paper explores the application of artificial intelligence technologies in addressing core challenges of blockchain systems, specifically in terms of scalability, security, and privacy protection. Instead of claiming a deep theoretical integration, we focus on how AI methods, such as machine learning and deep learning, have been effectively adopted to optimize blockchain consensus algorithms, improve smart contract vulnerability detection, and enhance privacy-preserving mechanisms like federated learning and differential privacy. Through comprehensive classification and discussion, this paper provides a structured overview of the current research landscape and identifies potential directions for further technical collaboration between AI and blockchain technologies.

Autonomous vehicles (AVs) promise substantial gains in safety, reliability, and decarbonization, yet safe and efficient interaction in dynamic, heterogeneous traffic remains a key barrier to large-scale deployment. Deep reinforcement learning (DRL) has emerged as a data-driven approach for learning adaptive decision policies that handle complex, unpredictable environments better than rule-based methods. However, different scenarios impose distinct requirements, necessitating scenario-specific algorithms. This survey systematically reviews DRL for four typical scenarios (highways, on-ramp merging, roundabouts, and unsignalized intersections), summarizes road features and recent advances, and evaluates methods using five criteria: driving safety, driving efficiency, training efficiency, unselfishness, and interpretability (DDTUI). Each DDTUI criterion is analyzed with respect to the reviewed algorithms. In addition, a dedicated scenario-centric learning transferability analysis is introduced that systematically evaluates whether each reviewed method demonstrates scene-specific learning improvements and assesses how effectively their designs transfer across the four scenarios. Finally, the challenges for future DRL-based decision-making algorithms are summarized.

Roundabout navigation presents significant challenges for autonomous vehicles (AVs) due to complex multi-vehicle interactions, highly dynamic and uncertain traffic patterns, and the requirement to coordinate with multiple vehicles simultaneously. Existing approaches often struggle to maintain safety and efficiency in uncertain vehicle behaviors and space-constrained situations, especially for mandatory lane changes and exit maneuvers. This paper presents a switched decision system that combines game theory and potential fields to enable robust navigation in roundabouts. The proposed approach first develops a Stackelberg game framework with uncertainty-aware prediction, where AVs dynamically adjust their decisions based on probabilistic estimates of surrounding vehicles' behaviors. Additionally, a hybrid potential field method is introduced that seamlessly transitions to emergency maneuvering when standard game-theoretic solutions become infeasible. Through extensive simulations under varied scenarios, the proposed approach achieves 97% successful navigation rates while maintaining collision rates below 1%, significantly outperforming baseline Nash equilibrium methods. The results demonstrate that integrating uncertainty characterization with strategic cooperation significantly improves the overall safety and efficiency of autonomous driving in roundabouts.

Decision making at unsignalized intersections presents significant challenges for autonomous vehicles (AVs), particularly in mixed traffic scenarios where both AVs and human-driven vehicles (HDVs) must safely coordinate their movements. This paper proposes a safety-critical multi-agent Monte Carlo tree search (MCTS) framework that integrates deterministic and probabilistic predictions to enable cooperative decision making in complex intersection scenarios. The framework incorporates three main innovations: 1) a safety assessment mechanism that systematically handles AV-to-AV (V2V), AV-to-HDV (V2H), and Vehicle-to-Road (V2R) interactions using dynamic safety thresholds and spatiotemporal risk metrics, 2)an adaptive HDV behavior awareness by combining the Intelligent Driver Model (IDM) with probabilistic distributions, and 3)a multi-objective reward function optimization approach that balances safety, efficiency, and cooperation. Extensive simulations demonstrate our framework’s efficacy and superior capability in ensuring safe and efficient intersection navigation across the fully-autonomous scenario (100% AVs) and challenging mixed traffic scenario (50% AVs +50% HDVs). Compared to benchmarks, our method reduces trajectory deviations by up to 37.56% in the fully-autonomous scenario and 62.43% in the mixed traffic scenario, while maintaining significantly lower Post-Encroachment Time (PET) violations (0% and 2.8%, respectively).

Safety and efficiency are crucial for autonomous driving in roundabouts, especially mixed traffic with both autonomous vehicles (AVs) and human-driven vehicles. This paper presents a learning-based algorithm that promotes safe and efficient driving across varying roundabout traffic conditions. A deep Q-learning network is used to learn optimal strategies in complex multi-vehicle roundabout scenarios, while a Kolmogorov-Arnold Network (KAN) improves the AVs’ environmental understanding. To further enhance safety, an action inspector filters unsafe actions, and a route planner optimizes driving efficiency. Moreover, model predictive control ensures stability and precision in execution. Experimental results demonstrate that the proposed system consistently outperforms state-of-the-art methods, achieving fewer collisions, reduced travel time, and stable training with smooth reward convergence.