In the context of autonomous vehicles, one of the most crucial tasks is to estimate the risk of the undertaken action. While navigating in complex urban environments, the Bayesian occupancy grid is one of the most popular types of map, where the information of occupancy is stored as the probability of collision. Although widely used, this kind of representation is not well suited for risk assessment: because of its discrete nature, the probability of collision becomes dependent on the tessellation size. Therefore, risk assessments on Bayesian occupancy grids cannot yield risks with meaningful physical units. In this article, we propose an alternative framework called Dynamic Lambda-Field that is able to assess physical risks in dynamic environments without being dependent on the tessellation size. Using our framework, we are able to plan safe trajectories where the risk function can be adjusted depending on the scenario. We validate our approach with quantitative experiments, showing the convergence speed of the grid and that the framework is suitable for real-world scenarios.
翻译:在自主车辆方面,最关键的任务之一是估计已采取行动的风险。在复杂的城市环境中航行时,贝叶斯人居住网是最受欢迎的地图类型之一,其中占用信息存储为碰撞的概率。虽然这种代表方式被广泛使用,但并不适合风险评估:由于其离散性质,碰撞的概率取决于熔岩大小。因此,对巴伊斯人居住网的风险评估不能用有意义的物理单位产生风险。在本条中,我们提议了一个称为动态兰巴达字段的替代框架,以便能够在动态环境中评估物理风险,而不必依赖熔岩大小。我们利用这个框架,能够规划安全轨道,根据情景调整风险功能。我们用定量实验来验证我们的方法,显示电网的趋同速度,并且框架适合现实世界的情景。