Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle’s lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, or whether the vehicle’s speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this article, we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle’s current lane. We employ a deep learning--based technique to classify the vehicle’s lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real-world datasets collected in developed and developing regions. DeepLane can detect a vehicle’s lane position with an accuracy of over 90%, and we have implemented DeepLane as an Android app.

中文翻译：

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使用深度神经网络在智能手机上进行车道检测

由于 GPS 精度差，当前基于智能手机的导航应用程序无法提供车道级信息。检测和跟踪车辆在道路上的车道位置有助于车道级导航。例如，重要的是要知道车辆是否在正确的车道上安全转弯，或者车辆的速度是否符合车道特定的速度限制。最近的努力已经使用道路网络信息和惯性传感器来估计车道位置。虽然惯性传感器可以检测到短窗口内的车道偏移，但随着时间的推移，它会受到误差累积的影响。在本文中，我们介绍了 DeepLane，该系统利用安装在挡风玻璃上的智能手机的后置摄像头来提供对车辆当前车道的准确估计。我们采用基于深度学习的技术对车辆的车道位置进行分类。DeepLane 不依赖任何基础设施支持，例如车道标线，即使在没有车道标线的情况下也能正常工作，这是发展中地区许多道路的特点。我们在发达和发展中地区收集的真实数据集上对 DeepLane 进行了广泛的评估。DeepLane 可以以超过 90% 的准确度检测车辆的车道位置，我们已将 DeepLane 实现为 Android 应用程序。