297 citations · 1.1k across the 40 of their papers we have counts for
16 papers · 1 filter
Discovering Avoidable Planner Failures of Autonomous Vehicles using Counterfactual Analysis in Behaviorally Diverse Simulation
Daisuke Nishiyama, Mario Ynocente Castro, Shirou Maruyama +7
Automated Vehicles require exhaustive testing in simulation to detect as many safety-critical failures as possible before deployment on public roads. In this work, we focus on the…
Behaviorally Diverse Traffic Simulation via Reinforcement Learning
Shinya Shiroshita, Shirou Maruyama, Daisuke Nishiyama +6
Traffic simulators are important tools in autonomous driving development. While continuous progress has been made to provide developers more options for modeling various traffic pa…
RAT iLQR: A Risk Auto-Tuning Controller to Optimally Account for Stochastic Model Mismatch
Haruki Nishimura, Negar Mehr, Adrien Gaidon +1
Successful robotic operation in stochastic environments relies on accurate characterization of the underlying probability distributions, yet this is often imperfect due to limited…
Monocular Differentiable Rendering for Self-Supervised 3D Object Detection
Deniz Beker, Hiroharu Kato, Mihai Adrian Morariu +4
3D object detection from monocular images is an ill-posed problem due to the projective entanglement of depth and scale. To overcome this ambiguity, we present a novel self-supervi…
Risk-Sensitive Sequential Action Control with Multi-Modal Human Trajectory Forecasting for Safe Crowd-Robot Interaction
Haruki Nishimura, Boris Ivanovic, Adrien Gaidon +2
This paper presents a novel online framework for safe crowd-robot interaction based on risk-sensitive stochastic optimal control, wherein the risk is modeled by the entropic risk m…
MATS: An Interpretable Trajectory Forecasting Representation for Planning and Control
Boris Ivanovic, Amine Elhafsi, Guy Rosman +2
Reasoning about human motion is a core component of modern human-robot interactive systems. In particular, one of the main uses of behavior prediction in autonomous systems is to i…