论文标题
使用传感器故障建模的车辆自我估计
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
论文作者
论文摘要
我们提出了一种类似于高速公路的场景的概率的自我估计算法，该算法旨在提高自我车道估计值的准确性，这只能依靠嘈杂的线探测器和跟踪器来获得。该贡献依赖于具有瞬态故障模型的隐藏马尔可夫模型（HMM）。拟议的算法利用了OpenStreetMap（或其他制图服务）Road Property Lane号码作为预期的车道数量和连续的杠杆数，可能是不完整的观察值。通过采用不同的线检测器来证明该算法的效率，并表明我们可以实现更多可用的，即在超过100公里的高速公路场景中，在意大利和西班牙都记录了超过100公里的高速公路场景。此外，由于我们找不到与其他方法进行定量比较的合适数据集，因此我们收集了数据集并手动注释了有关车辆自我车道的地面真相。此类数据集可公开可从科学界使用。
We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relying only on a noisy line detector and tracker. The contribution relies on a Hidden Markov Model (HMM) with a transient failure model. The proposed algorithm exploits the OpenStreetMap (or other cartographic services) road property lane number as the expected number of lanes and leverages consecutive, possibly incomplete, observations. The algorithm effectiveness is proven by employing different line detectors and showing we could achieve much more usable, i.e. stable and reliable, ego-lane estimates over more than 100 Km of highway scenarios, recorded both in Italy and Spain. Moreover, as we could not find a suitable dataset for a quantitative comparison with other approaches, we collected datasets and manually annotated the Ground Truth about the vehicle ego-lane. Such datasets are made publicly available for usage from the scientific community.
