At a certain time, to forecast a reasonable future trajectory, each agent needs to pay attention to the interactions with only a small group of most relevant agents instead of unnecessarily paying attention to all the other agents.
We constantly integrate our knowledge and understanding of the world to enhance our interpretation of what we see.
Language allows humans to build mental models that interpret what is happening around them resulting in more accurate long-term predictions.
When predicting trajectories of road agents, motion predictors usually approximate the future distribution by a limited number of samples.
Our model takes as input scene information in the form of a video and noisy gaze estimates, and outputs visual saliency, a refined gaze estimate, and an estimate of the person's attended awareness.
Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction.
In this paper, we propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic and produce risk-bounded policies efficiently by adding an extra risk term in the loss function of the policy network.
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation.
no code implementations • 24 Nov 2020 • Daisuke Nishiyama, Mario Ynocente Castro, Shirou Maruyama, Shinya Shiroshita, Karim Hamzaoui, Yi Ouyang, Guy Rosman, Jonathan DeCastro, Kuan-Hui Lee, Adrien Gaidon
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads.
Traffic simulators are important tools in autonomous driving development.
Reasoning about human motion is a core component of modern human-robot interactive systems.
With the understanding of the complete surgical workflow, the robots are able to assist the surgeons in intra-operative events, such as by giving a warning when the surgeon is entering specific keys or high-risk phases.
In this work, we propose a behavioral cloning approach that can safely leverage imperfect perception without being conservative.
To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-ReIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes.
Predicting driver intentions is a difficult and crucial task for advanced driver assistance systems.
Our main contribution is the concept of learning context maps to improve the prediction task.
Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems.
In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors.
We define a novel variational network capable of learning from raw camera data of the environment as well as higher level roadmaps to predict (1) a full probability distribution over the possible control commands; and (2) a deterministic control command capable of navigating on the route specified within the map.
We demonstrate the utility of this model for inference tasks such as activity detection, classification, and summarization.
We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion.
We consider the problem of computing optimal segmentation of such signals by k-piecewise linear function, using only one pass over the data by maintaining a coreset for the signal.
Traditional approaches to scene representation exploit this phenomenon via the somewhat restrictive assumption that every plane is perpendicular to one of the axes of a single coordinate system.