Ramp merging areas, as critical nodes for intelligent vehicle traffic, are characterized by high dynamic interaction and strong uncertainty, making it difficult for traditional path planning algorithms to balance safety, real-time performance, and smoothness in such complex scenarios. To address these challenges, a path planning algorithm is proposed integrating an improved artificial potential field and line-of-sight A^*. Firstly, a piecewise potential field model comprising guidance, obstacle avoidance, and boundary constraints is constructed. A dynamic weight adjustment mechanism is also introduced to adapt to the kinematic requirements of vehicles at different merging stages, efficiently resolving the unreachable target problem of traditional potential field methods.Secondly, a physical line-of-sight check mechanism incorporating safety thresholds is embedded into the A^* algorithm. By utilizing a non-axial connection strategy to prune redundant nodes, the node expansion direction is optimized to enhance search efficiency. Finally, the improved potential field is mapped as the heuristic cost function of the A^* algorithm, achieving a deep coupling of global path planning and local risk perception. Simulation results demonstrate that, compared with traditional A^*, the Rapidly-exploring Random Tree Star (RRT^*) algorithm, and existing analogous fusion schemes, the runtime of the proposed algorithm is reduced by 70.8%, the minimum obstacle avoidance distance is increased by 106.6%, and lateral jerk is decreased to 0.62 m/s³. The proposed algorithm effectively enhances the safety and stability of trajectory planning, demonstrating its efficiency and robustness in complex merging scenarios.

The ever-increasing functionalities and escalating complexity of existing Advanced Driver Assistance Systems inevitably cause the problem of Safety of The Intended Functionality. The identification and generation of trigger conditions play a critical role in SOTIF activities. Most existing trigger condition identification approachesare mainly based on the System-Theoretic Process Analysis method, which however neglect the issues within the system's functional state transitions. This paper adopts a knowledge-driven approach to construct a trigger condition identification mechanism by integrating STPA and Finite State Machine theories to establish an expanded system control structure. Safety analysis is conducted concerning the expanded control architecture and functional state transitions. By considering system limitations and human misuse, trigger conditions are identified, generated, described, classified, and labeled. Finally, the proposed trigger condition generation mechanism is applied to an Integrated Cruise Assistance system, obtaining trigger conditions and their classifications. The generated mechanism is compared with existing trigger condition generation methods, demonstrating its practicality, feasibility, and effectiveness.