Detecting Intentions of Vulnerable Road Users Based onCollective Intelligence as a Basis for Automated Driving

DeCoInt2项目目前的更新提案侧重于使用协作技术对自动驾驶中弱势道路使用者（vru）的意图检测。特别是在城市地区，行人和骑自行车者等vru仍将在未来的混合交通中发挥重要作用。为了实现无事故的自动驾驶交通，感知虚拟车辆不仅重要，而且检测其意图也至关重要。意图检测包括基本的动作检测，如站立、移动、转弯以及对未来轨迹的预测。我们的目标是进一步为我们设想的未来交通场景做出贡献，在未来的交通场景中，配备摄像头和雷达等传感器、地图、Car-2X通信功能、配备传感器的基础设施和vru本身（如果配备智能手机等智能设备）的车辆的集体智能被用来检测涉及vru的潜在危险情况，在受影响的实体面临这些情况之前。在第一阶段，我们对未来交通场景的贡献是协作式VRU意图检测，包括协作感知、基本运动检测和轨迹预测。我们实现了对vru的鲁棒跟踪，通过vru所佩戴的智能设备等多个交通参与者协同解决闭塞问题。通过我们的合作方法，即使在能见度有限或环境条件恶劣的情况下，我们也能实现快速而强大的基本运动检测，从而实现准确的VRU轨迹预测。在DeCoInt2项目的第二阶段，我们将考虑以下三个新的方面：上下文信息的识别和集成、概率VRU轨迹预测以及态势分析和预测。此外，我们将扩展我们在协同跟踪、基本运动预测和智能设备集成方面的工作。我们的目标是整合上下文信息（例如，几何信息，如自行车道的路线或交通规则）来改进意图检测过程，从而实现更精确的预测。结合情境信息的协同感知结果、协同基本运动预测结果和以预测概率分布形式呈现的协同概率轨迹预测结果，是态势分析和预测的重要组成部分，而态势分析和预测又是轨迹规划的关键。总之，我们的目标是在一个真实的交通环境中在线进行的选定示例场景中评估我们的合作方法。

The present renewal proposal of the project DeCoInt2 focuses on intention detection of vulnerable road users (VRUs) in automated driving using cooperative technologies. Especially in urban areas, VRUs such as pedestrians and cyclists will still play an important role in the mixed traffic of tomorrow. For an accident-free traffic with automated vehicles, it is not just important to perceive VRUs but it is also essential that their intentions are detected. The intention detection consists of basic movement detection, e.g., standing, moving, turning, and a forecast of the future trajectory. We aim to further contribute to our envisioned future traffic scenarios, in which the collective intelligence of vehicles equipped with sensors such as cameras and radar, maps, and Car-2X communication capabilities, sensor-equipped infrastructure, and VRUs themselves (if equipped with smart devices such as smartphones) are used to detect potentially dangerous situations involving VRUs, before the affected entities face these situations. In the first phase, our contribution to that envisioned future traffic scenario was cooperative VRU intention detection including cooperative perception, basic movement detection, and trajectory forecast. We managed to realize a robust tracking of VRUs, resolving occlusions by means of multiple traffic participants including smart devices worn by VRUs in a cooperative way. Using our cooperative approach, we achieved fast and robust basic movement detection even in situations with limited visibility or bad environmental conditions, enabling an accurate VRU trajectory forecast. In the second phase of the project DeCoInt2, we will consider the following three new aspects: identification and integration of context information, probabilistic VRU trajectory forecasts, and situation analysis and prediction. Additionally, we will extend our work on cooperative tracking, basic movement forecasting, and integration of smart devices. We aim to integrate context information (e.g., geometric information such as a course of a bicycle path or traffic regulations) to improve the intention detection process, allowing for more precise forecasts. The combined results of the cooperative perception incorporating context information, cooperative basic movement forecasts, and cooperative probabilistic trajectory forecasts, which take the form of predictive probability distributions, serve as an important ingredient for situation analysis and prediction, which in turn is essential for trajectory planning. Altogether, we aim to evaluate our cooperative approach in selected sample scenarios conducted on-line in a real traffic environment.