With development of information techniques in navigation and shipping, machine learning
algorithms are applied in enhancing navigation safety. One of critical areas, which attracts …

Motion prediction is a challenging task for autonomous vehicles due to uncertainty in the
sensor data, the non-deterministic nature of future, and complex behavior of agents. In this …

Humans navigate in their environment by learning a mental model of the world through
passive observation and active interaction. Their world model allows them to anticipate what …

In this article, we present the circular accessible depth (CAD), a robust traversability
representation for an unmanned ground vehicle (UGV) to learn traversability in various …

This paper investigates methods for enhancing the quality of occupancy grid maps (OGMs)
using a combination of a self-supervised data generation procedure using only unlabeled …

This paper presents two variations of a novel stochastic prediction algorithm that enables
mobile robots to accurately and robustly predict the future state of complex dynamic scenes …

Manually specifying features that capture the diversity in traffic environments is impractical.
Consequently, learning-based agents cannot realize their full potential as neural motion …

In this paper we investigate the effect of the unpredictability of surrounding cars on an ego-
car performing a driving maneuver. We use Maximum Entropy Inverse reinforcement …

We present a method for generating, predicting, and using spatiotemporal occupancy grid
maps (SOGM), which embed future semantic information of real dynamic scenes. We …

The irregularity and permutation invariance of point cloud data pose challenges for effective
learning. Conventional methods for addressing this issue involve converting raw point …