Predicting other traffic participants trajectories is a crucial task for an autonomous vehicle, in order to avoid collisions on its planned trajectory. It is also necessary for many Advanced Driver Assistance Systems, where the egovehicle's trajectory has to be predicted too. Even if trajectory prediction is not a deterministic task, it is possible to point out the most likely trajectory. This paper presents a new trajectory prediction method which combines a trajectory prediction based on Constant Yaw Rate and Acceleration motion model and a trajectory prediction based on maneuver recognition. It takes benefit on the accuracy of both predictions respectively a short-term and long-term. The defined Maneuver Recognition Module selects the current maneuver from a predefined set by comparing the center lines of the road's lanes to a local curvilinear model of the path of the vehicle. The overall approach was tested on prerecorded human real driving data and results show that the Maneuver Recognition Module has a high success rate and that the final trajectory prediction has a better accuracy.
Abstract-In urban environments, moving obstacles detection and free space determination are key issues for driving assistance systems and autonomous vehicles. When using lidar sensors scanning in front of the vehicle, uncertainty arises from ignorance and errors. Ignorance is due to the perception of new areas and errors come from imprecise pose estimation and noisy measurements. Complexity is also increased when the lidar provides multi-echo and multi-layer information. This paper presents an occupancy grid framework that has been designed to manage these different sources of uncertainty. A way to address this problem is to use grids projected onto the road surface in global and local frames. The global one generates the mapping and the local one is used to deal with moving objects. A credibilist approach is used to model the sensor information and to do a global fusion with the worldfixed map. Outdoor experimental results carried out with a precise positioning system show that such a perception strategy increases significantly the performance compared to a standard approach.
Advanced Driving Assistance Systems exploit exteroceptive sensors to help the driver in perceiving the dynamic environment, like other vehicles or pedestrians. This paper proposes an original approach to deal with this perception challenge in urban environments. The method detects mobile objects motions using grids elaborated thanks to a lidar range scanner and an enhanced map of the drivable space. The data fusion is performed using the Dempster-Shafer theory which provides an interesting framework particularly well adapted to manage the uncertainties of the sensors. By analyzing conflicting information, objects movements can be efficiently characterized. This formalism provides also the interesting possibility to introduce decay factors that are useful for forgetting old information. Experimental results obtained with an IBEO Alasca and an Applanix positioning system show that such a perception strategy can be effective compared to deterministic accumulation strategies.
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