For highly available automated driving, a robust road estimation is indispensable. In order to tackle the challenges of this task, many works employ a fusion of multiple sources, e.g., visually detected lane markings, leading vehicle, digital maps, etc. However, each source has certain advantages and drawbacks depending on the operational scenarios. Hence, the assumption made by many existing approaches that the sources always are equally reliable for the fusion process is inappropriate. Therefore, this work proposes a novel concept by incorporating reliabilities into the multisource fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the reliability for each source is estimated online using classifiers trained with the sensor measurements, the past performance, and the context. Using real data recordings, experimental results show that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion.

中文翻译：

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使用在线估计可靠性进行稳健道路检测的多源融合


对于高度可用的自动驾驶，强大的道路估计是必不可少的。为了应对这项任务的挑战，许多工作采用了多种来源的融合，例如视觉检测的车道标记、领先车辆、数字地图等。然而，根据操作场景，每种来源都有一定的优点和缺点。因此，许多现有方法做出的假设，即源对于融合过程始终同样可靠，是不合适的。因此，这项工作提出了一个新颖的概念，将可靠性纳入多源融合中，以便道路估计任务可以交替选择最可靠的源。因此，使用通过传感器测量、过去的性能和上下文训练的分类器在线估计每个源的可靠性。使用真实数据记录，实验结果表明，与平均融合相比，所提出的可靠性感知融合将自动驾驶的可用性提高了 7 个百分点。