Robot navigation in crowded public spaces is a complex task that requires addressing a
variety of engineering and human factors challenges. These challenges have motivated a …

This paper provides a literature review of some of the most important concepts, techniques,
and methodologies used within autonomous car systems. Specifically, we focus on two …

Due to the stochasticity of human behaviors, predicting the future trajectories of road agents
is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction …

Motion forecasting for autonomous driving is a challenging task because complex driving
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …

Predicting the future behavior of road users is one of the most challenging and important
problems in autonomous driving. Applying deep learning to this problem requires fusing …

Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory
prediction system to model the multi-modality of future motion states. Unlike existing …

Learning to predict agent motions with relationship reasoning is important for many
applications. In motion prediction tasks, maintaining motion equivariance under Euclidean …

Pedestrian trajectory prediction is a key technology in autopilot, which remains to be very
challenging due to complex interactions between pedestrians. However, previous works …

High-definition maps (HD maps) are a key component of most modern self-driving systems
due to their valuable semantic and geometric information. Unfortunately, building HD maps …

Trajectory prediction has been widely pursued in many fields, and many model-based and
model-free methods have been explored. The former include rule-based, geometric or …