AI 中文总结
该研究开发了一种部署在 Raspberry Pi 和 Flutter 移动应用上的 SafeStudent Driving 多模态系统，通过计算机视觉与移动感知技术辅助青少年驾驶员，实验验证了其有效性，为新手驾驶员安全习惯养成提供了低成本方案。
AI 中文摘要
青少年驾驶员的事故率高得不成比例，通常是由于缺乏经验和对基本交通规则的注意力不集中。SafeStudent Driving 解决了这一问题，该系统是部署在 Raspberry Pi 设备和基于 Flutter 的移动应用上的多模态指导系统。该系统使用三个基于 YOLO 的计算机视觉模型来检测交通信号灯、灯泡颜色和道路标志，一个 OCR 模块来读取限速值，以及一个音频模型加 IMU 数据来推断转弯时是否使用了转向灯。分析层会随时间平滑检测结果，并通过文本转语音或预录制音频触发优先级语音提示。关键挑战包括在不同光照下实现足够的模型精度、在有限硬件上实现足够快的推理速度，以及设计既能提供信息又不分散驾驶员注意力的提示。对标志检测和转向灯识别的实验突出了系统的优势和失效模式，为未来的改进提供了指导。总体而言，该项目展示了一种实用、低成本的方式，帮助新手驾驶员在实际交通中养成更安全的习惯。
英文摘要
Teen drivers face disproportionately high crash rates, often due to inexperience and inconsistent attention to basic traffic rules. SafeStudent Driving addresses this problem with a multimodal coaching system deployed on both a Raspberry Pi device and a Flutter-based mobile app. The system uses three YOLO-based computer-vision models to detect traffic lights, light-bulb colors, and road signs, an OCR module to read speed-limit values, and an audio model plus IMU data to infer whether turn signals are used during turns. An analysis layer smooths detections over time and triggers prioritized voice prompts through text-to-speech or pre-recorded audio. Key challenges included achieving sufficient model accuracy in varied lighting, running inference fast enough on limited hardware, and designing prompts that inform without distracting the driver [3]. Experiments on sign detection and turn-signal recognition highlight strengths and failure modes, guiding future improvements. Overall, the project demonstrates a practical, low-cost way to help novice drivers build safer habits in real traffic.
