In order to meet the application requirements of autonomous vehicles,this paper proposes a simultaneous localization and mapping(SLAM)algorithm,which uses a VoxelGrid filter to down sample the point cloud data,with the combination of iterative closest points(ICP)algorithm and Gaussian model for particles updating,the matching between the local map and the global map to quantify particles' importance weight.The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles' poses,which would decrease particle numbers,increase algorithm speed and restrain particles' impoverishment.The calculation of particles' importance weight based on matching of attribute between grid maps is simple and practicable.Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.
Intersections are quite important and complex traffic scenarios,where the future motion of surrounding vehicles is an indispensable reference factor for the decision-making or path planning of autonomous vehicles.Considering that the motion trajectory of a vehicle at an intersection partly obeys the statistical law of historical data once its driving intention is determined,this paper proposes a long short-term memory based(LSTM-based)framework that combines intention prediction and trajectory prediction together.First,we build an intersection prior trajectories model(IPTM)by clustering and statistically analyzing a large number of prior traffic flow trajectories.The prior trajectories model with fitted probabilistic density is used to approximate the distribution of the predicted trajectory,and also serves as a reference for credibility evaluation.Second,we conduct the intention prediction through another LSTM model and regard it as a crucial cue for a trajectory forecast at the early stage.Furthermore,the predicted intention is also a key that is associated with the prior trajectories model.The proposed framework is validated on two publically released datasets,next generation simulation(NGSIM)and INTERACTION.Compared with other prediction methods,our framework is able to sample a trajectory from the estimated distribution,with its accuracy improved by about 20%.Finally,the credibility evaluation,which is based on the prior trajectories model,makes the framework more practical in the real-world applications.
Ting ZhangWenjie SongMengyin FuYi YangMeiling Wang
