Interaction|driven Behavior Prediction and Planning for Autonomous ...

Planning-Oriented Autonomous Driving - CVF Open Access

An intuitive resolution would be to perceive surrounding objects, predict future behaviors and plan a safe maneuver explicitly, as illustrated in Fig. 1(c.2) ...

Social behavior for autonomous vehicles - PNAS

We solve the dynamic game by finding the Nash equilibrium, yielding an online method of predicting multiagent interactions given their SVOs.

Multi-agent learning for safe and efficient autonomous vehicles

... behavior prediction. The robustness of MARL is central to this research, particularly under conditions where state information may be ...

Learning-enabled decision-making for autonomous driving - DR-NTU

A comprehensive behavior planning framework that integrates all three core modules is proposed. It generates diverse trajectory proposals, forecasts multi ...

Motion Planning Autonomous Driving - Chair of Automatic Control

... planning is predicting the behavior of the surrounding road users. The ... Data-driven methods help to generate probability distributions of possible behaviors.

Workshop on Autonomous Driving

... autonomy, including perception, behavior prediction and motion planning. In this full-day workshop, our keynote speakers will provide ...

INTERACTION Dataset - Papers With Code

... behavior/motion prediction, 2) behavior cloning and ... Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model.

Efficient Game-Theoretic Planning with Prediction ... - NASA ADS

Planning under social interactions with other agents is an essential problem for autonomous ... behavior in highly interactive scenarios.

Behavior Trees for Path Planning (Autonomous Driving) - Medium

prediction about what static and dynamic obstacles are likely to do. Output: Suggested maneuver for the vehicle which the trajectory planner is ...

Interaction Dataset

1) intention/behavior/motion prediction, · 2) behavior cloning, imitation learning, and inverse reinforcement learning, · 3) behavior analysis and modeling, · 4) ...

Interpretable Goal-based Prediction and Planning for Autonomous ...

... behavior prediction: Theory ... Automated driving in uncertain environments: Planning with interaction and uncertain maneuver prediction.

Anticipating others' behavior on the road | MIT News

A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.

Careers | Join our team | Applied Intuition

Off-road autonomy stack · Trucking autonomy stack. Products. ADAS and AD development platform. Simulation. Object Sim. Prediction, planning & controls ...

Interaction-Aware Motion Prediction for Autonomous Driving

driving application that employs predictive planning ... Mouzakitis,. “Deep learning-based vehicle behavior prediction for autonomous driving.

Multipolicy Decision-Making for Autonomous Driving via ...

Multipolicy Decision-Making via Changepoint-based Behavior Prediction: Theory and Experiment. 13. Normalized Policy Reward vs. Time (Sim). Norm.

What is AI? Artificial Intelligence Explained - TechTarget

Machine learning enables software to autonomously learn patterns and predict outcomes by using historical data as input. ... This type of AI can infer human ...

Multi‐future Transformer: Learning diverse interaction modes for ...

Predicting the future behaviour of neighbouring agents is crucial for autonomous driving. This task is challenging, largely because of the ...

The Theory of Planned Behavior - sph.bu.edu - Boston University

The TPB has been used successfully to predict and explain a wide range of health behaviors and intentions including smoking, drinking, health ...

What Is Artificial Intelligence (AI)? - IBM

... predictions and reliable, data-driven decisions. Combined with ... behavior, accuracy and performance. Operational risks. Like all ...

AI—The good, the bad, and the scary | Engineering | Virginia Tech

Losey explores the intersection of human-robot interaction by developing learning and control algorithms that create intelligent, proactive, and adaptable ...