Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, Shaanxi, China.
School of Software Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Xi'an 710049, Shaanxi, China.
Sensors (Basel). 2019 Apr 8;19(7):1670. doi: 10.3390/s19071670.
Driving behavior is the main basis for evaluating the performance of an unmanned vehicle. In simulation tests of unmanned vehicles, in order for simulation results to be approximated to the actual results as much as possible, model of driving behaviors must be able to exhibit actual motion of unmanned vehicles. We propose an automatic approach of simulating dynamic driving behaviors of vehicles in traffic scene represented by image sequences. The spatial topological attributes and appearance attributes of virtual vehicles are computed separately according to the constraint of geometric consistency of sparse 3D space organized by image sequence. To achieve this goal, we need to solve three main problems: Registration of vehicle in a 3D space of road environment, vehicle's image observed from corresponding viewpoint in the road scene, and consistency of the vehicle and the road environment. After the proposed method was embedded in a scene browser, a typical traffic scene including the intersections was chosen for a virtual vehicle to execute the driving tasks of lane change, overtaking, slowing down and stop, right turn, and U-turn. The experimental results show that different driving behaviors of vehicles in typical traffic scene can be exhibited smoothly and realistically. Our method can also be used for generating simulation data of traffic scenes that are difficult to collect.
驾驶行为是评价无人驾驶车辆性能的主要依据。在无人驾驶车辆的仿真测试中，为了使仿真结果尽可能接近实际结果，驾驶行为模型必须能够表现出无人驾驶车辆的实际运动。我们提出了一种基于图像序列表示的交通场景中车辆动态驾驶行为的自动仿真方法。根据由图像序列组织的稀疏 3D 空间的几何一致性约束，分别计算虚拟车辆的空间拓扑属性和外观属性。为了实现这一目标，我们需要解决三个主要问题：车辆在道路环境 3D 空间中的注册、从道路场景中相应视点观察到的车辆图像，以及车辆与道路环境的一致性。在将所提出的方法嵌入到场景浏览器后，选择了一个包含交叉口的典型交通场景，让一辆虚拟车辆执行变道、超车、减速停车、右转和掉头等驾驶任务。实验结果表明，典型交通场景中不同车辆的驾驶行为可以平稳、真实地展现。我们的方法还可以用于生成难以采集的交通场景的仿真数据。
