Current autonomous driving technology typically relies on high-precision (HD) maps to ensure safe, reliable, and accurate navigation in urban environments. While these maps provide essential road information, their creation and maintenance are costly, limiting their widespread application. To mitigate this reliance, we propose a novel system, Language-Assisted Continuous Navigation in Structured Spaces (LACNS). LACNS facilitates autonomous driving without the need for HD maps by integrating vehicle-centric local perception with real-time language instructions from map software or human navigators. LACNS begins by generating a BEV map using the vehicle's front-facing camera. Simultaneously, a pretrained Visual Language Model (VLM) detects intersections from the camera images, assigning a score to each. Road elements are then extracted from the BEV map and combined with the intersection scores to identify potential navigation frontiers. Language instructions, processed by a pretrained Large Language Model(LLM), are used to select the most suitable frontier. Finally, the chosen frontier and BEV map are employed to plan a safe route and control the vehicle's movement. We evaluated LACNS using the Carla simulator to validate its navigation capabilities in continuous spaces. Initial experiments involved navigating through four intersections with varying directional instructions, where LACNS demonstrated high and consistent success rates across multiple trials. Further simulations in real-time navigation scenarios revealed that LACNS consistently maintained a high success rate across three progressively challenging routes. These results highlight the effectiveness of our novel autonomous driving navigation method without HD maps.