Safety assurances for human-robot interaction via confidence-aware game-theoretic human models
An outstanding challenge with safety methods for human-robot interaction is reducing their
conservatism while maintaining robustness to variations in human behavior. In this work, we …
conservatism while maintaining robustness to variations in human behavior. In this work, we …
Graph and recurrent neural network-based vehicle trajectory prediction for highway driving
Integrating trajectory prediction to the decision-making and planning modules of modular
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
autonomous driving systems is expected to improve the safety and efficiency of self-driving …
Predictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …
[HTML][HTML] Injecting knowledge in data-driven vehicle trajectory predictors
Vehicle trajectory prediction tasks have been commonly tackled from two distinct
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
perspectives: either with knowledge-driven methods or more recently with data-driven ones …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
V Bharilya, N Kumar - Vehicular Communications, 2024 - Elsevier
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Maximum-entropy multi-agent dynamic games: Forward and inverse solutions
In this article, we study the problem of multiple stochastic agents interacting in a dynamic
game scenario with continuous state and action spaces. We define a new notion of …
game scenario with continuous state and action spaces. We define a new notion of …
A survey on datasets for the decision making of autonomous vehicles
Autonomous vehicles (AVs) are expected to reshape future transportation systems, and
decision making is one of the critical modules toward high-level automated driving. To …
decision making is one of the critical modules toward high-level automated driving. To …
Pedestrian behavior prediction using deep learning methods for urban scenarios: A review
The prediction of pedestrian behavior is essential for automated driving in urban traffic and
has attracted increasing attention in the vehicle industry. This task is challenging because …
has attracted increasing attention in the vehicle industry. This task is challenging because …
Hpnet: Dynamic trajectory forecasting with historical prediction attention
Predicting the trajectories of road agents is essential for autonomous driving systems. The
recent mainstream methods follow a static paradigm which predicts the future trajectory by …
recent mainstream methods follow a static paradigm which predicts the future trajectory by …
Behavioral intention prediction in driving scenes: A survey
In driving scenes, road agents often engage in frequent interaction and strive to understand
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …
their surroundings. Ego-agent (each road agent itself) predicts what behavior will be …