论文标题
tridentnetv2：动态轨迹生成的轻量级图形全球计划表示
TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation
论文作者
论文摘要
我们为自主导航提供动态轨迹生成的框架，该框架不依赖HD地图作为基础表示。高清晰度（HD）地图已成为大多数自动驾驶框架的关键组成部分，其中包括以厘米级别注释的完整道路网络信息，其中包括可遍布的航路点，车道信息和交通信号。取而代之的是，鉴于基于名义图的全球计划和轻巧的场景表示形式，提出的方法将实时实时地模拟可行的以自我为中心的轨迹的分布。通过嵌入上下文信息，例如人行横道，停止标志和交通信号，我们的方法在包括各种相交操作的多个城市导航数据集中达到了较低的错误，同时保持实时性能和降低网络复杂性。引入的基础数据集可在线提供。
We present a framework for dynamic trajectory generation for autonomous navigation, which does not rely on HD maps as the underlying representation. High Definition (HD) maps have become a key component in most autonomous driving frameworks, which include complete road network information annotated at a centimeter-level that include traversable waypoints, lane information, and traffic signals. Instead, the presented approach models the distributions of feasible ego-centric trajectories in real-time given a nominal graph-based global plan and a lightweight scene representation. By embedding contextual information, such as crosswalks, stop signs, and traffic signals, our approach achieves low errors across multiple urban navigation datasets that include diverse intersection maneuvers, while maintaining real-time performance and reducing network complexity. Underlying datasets introduced are available online.
