Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
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From computer vision and speech recognition to forecasting trajectories in autonomous vehicles, deep learning approaches are at the forefront of so many domains.
Towards this end it is necessary that we have a comprehensive modeling framework for decision-making within which human driving preferences can be inferred statistically from observed driving behaviors in realistic and naturalistic traffic settings.
An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection.
Taking the low-resolution LiDAR point cloud and the monocular image as input, our depth completion network is able to produce dense point cloud that is subsequently processed by a voxel-based network for 3D object detection.
Our results confirm that sensor placement is an important factor in 3D point cloud-based object detection and could lead to a variation of performance by 10% ~ 20% on the state-of-the-art perception algorithms.
Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined.
Results show that participants' constraints improved the expected return of the plans by 10% ($p < 0. 05$) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience.