Autonomous vehicles (AVs) are anticipated to supersede human drivers with an expectation of improved safety and operation. Since current infrastructure is designed based on the constraints caused by human drivers, it must be reassessed for autonomous driving compatibility. Recently, representatives from the infrastructure owners/operators (IOOs), automotive industry, and academia have advocated for new approaches to prepare roadways for the deployment of AVs over the next decade. Following these recommendations, this paper proposes a novel, simulation-based approach for the assessment of highways "readiness" for AVs using 3D point cloud data. The proposed method uses octrees to perform volumetric queries for potential obstructions within an AV sensory field. The proposed approach is compared to a state-of-the-art raycasting approach. Consequently, available sight distances and maximum safe speed limits based on road and AV characteristics are proposed. Finally, a discussion of the potential mitigation measures at the locations with limited sight distances is presented.

中文翻译：

---

基于八叉树的点云模拟评估自动驾驶汽车高速公路基础设施的准备情况

预计自动驾驶汽车 (AV) 将取代人类驾驶员，并期望提高安全性和操作性。由于当前的基础设施是根据人类驾驶员造成的限制设计的，因此必须重新评估自动驾驶的兼容性。最近，来自基础设施所有者/运营商 (IOO)、汽车行业和学术界的代表倡导采用新方法为未来十年的自动驾驶汽车部署做好道路准备。根据这些建议，本文提出了一种新颖的、基于模拟的方法，用于使用 3D 点云数据评估自动驾驶汽车的高速公路“准备情况”。所提出的方法使用八叉树对 AV 感觉场内的潜在障碍物执行体积查询。将所提出的方法与最先进的光线投射方法进行比较。因此，提出了基于道路和自动驾驶特性的可用视距和最大安全速度限制。最后，讨论了在视距有限的位置可能采取的缓解措施。