Object detection in autonomous driving consists in perceiving and locating
instances of objects in multi-dimensional data, such as images or lidar scans.
Very recently, multiple works are proposing to evaluate object detectors by
measuring their ability to detect the objects that are most likely to interfere
with the driving task. Detectors are then ranked according to their ability to
detect the most relevant objects, rather than the highest number of objects.
However there is little evidence so far that the relevance of predicted object
may contribute to the safety and reliability improvement of the driving task.
This position paper elaborates on a strategy, together with partial results, to
i) configure and deploy object detectors that successfully extract knowledge on
object relevance, and ii) use such knowledge to improve the trajectory planning
task. We show that, given an object detector, filtering objects based on their
relevance, in combination with the traditional confidence threshold, reduces
the risk of missing relevant objects, decreases the likelihood of dangerous
trajectories, and improves the quality of trajectories in general.

配置和部署能够成功提取对象相关性知识的对象检测器，并利用这些知识来改善轨迹规划任务。在给定物体检测器的情况下，基于对象的相关性和传统的置信度阈值进行过滤，可以减少错过相关对象的风险，降低危险轨迹的可能性，并总体上提高轨迹的质量。