Abstract
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.
Highlights
Evaluation of the intelligence level and comprehensive performance of unmanned vehicles turns to ontology and phenomenology
On the basis of obtained trail of viewpoint locations corresponding to image sequences, geographic information information system system (GIS) data of rthe road can be obtained by GIS
For the purpose of further verification of our method, we embed the algorithm in a scene browser and choose a typical traffic scene including the intersections for virtual vehicle to execute the driving tasks of lane change, overtaking, slowing down and stop, right turn, and U-turn
Summary
Evaluation of the intelligence level and comprehensive performance of unmanned vehicles turns to ontology and phenomenology. Wescene use thewith real multi-sensor data captured from the real traffic environment augment the the traffic with vehicles in different driving behaviors. Simulation of driving behaviors with image sequences collected from real road environment. The scene for testing the unmanned vehicles is topology relationship between the virtual vehicles and the road environment. The scene for testing the the unmanned vehicles is an image sequence containing spatial topology information. As part of our work on parallel testing of vehicle intelligence [2], we propose an automatic appearance attributes of virtual vehicles are computed separately according to the constraint of approach of simulating dynamic driving behaviors of vehicles. Traffic Scene: Unmanned vehicles should be tested in a typical traffic environment including static, dynamic, and uncertain factors such urban and rural roads. We combine 3D model with multi-viewpoints corresponding to different vehicle images
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