8 accepted papers
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the i…
When predicting trajectories of road agents, motion predictors often approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specifications. However, existing predictors are often optimized…
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions. We present a novel trajectory prediction model that uses linguistic intermediate representations to forecast trajectories, and is trained using trajectory samp…
Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems. Traditional confidence measures on predictions often ignore the way predicted trajectories affect downstream decisions for safe driving. In this letter, we propose a novel multi-task intent recogniti
Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it - a key ability for evaluating safety from a planning and verificatio
Among traffic accidents in the USA, 23% of fatal and 32% of non-fatal incidents occurred at intersections. For driver assistance systems, intersection navigation remains a difficult problem that is critically important to increasing driver safety. In this letter, we examine how to navigate an unsign
Predicting the motion of a driver’s vehicle is crucial for advanced driving systems, enabling detection of potential risks towards shared control between the driver and automation systems. In this paper, we propose a variational neural network approach that predicts future driver trajectory distribu…
Motion planning for high degree of freedom (DOF) robots is not an easy task, and often requires optimization in a high dimensional space. Still, a generic motion planner using a single cost function for optimization may not be optimal over a number of different tasks with various task specific const