Search Results for author: Ransalu Senanayake

Found 16 papers, 6 papers with code

Model Predictive Optimized Path Integral Strategies

1 code implementation30 Mar 2022 Dylan M. Asmar, Ransalu Senanayake, Shawn Manuel, Mykel J. Kochenderfer

We generalize the derivation of model predictive path integral control (MPPI) to allow for a single joint distribution across controls in the control sequence.

How Do We Fail? Stress Testing Perception in Autonomous Vehicles

1 code implementation26 Mar 2022 Harrison Delecki, Masha Itkina, Bernard Lange, Ransalu Senanayake, Mykel J. Kochenderfer

This paper presents a method for characterizing failures of LiDAR-based perception systems for AVs in adverse weather conditions.

Autonomous Vehicles Data Augmentation +2

CoCo Games: Graphical Game-Theoretic Swarm Control for Communication-Aware Coverage

no code implementations8 Nov 2021 Malintha Fernando, Ransalu Senanayake, Martin Swany

We propose a novel framework for real-time communication-aware coverage control in networked robot swarms.

Variational Inference

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

1 code implementation NeurIPS 2021 Phil Chen, Masha Itkina, Ransalu Senanayake, Mykel J. Kochenderfer

We evaluate our method on a variety of generative models, including variational autoencoders and auto-regressive architectures.

A Hybrid Rule-Based and Data-Driven Approach to Driver Modeling through Particle Filtering

no code implementations29 Aug 2021 Raunak Bhattacharyya, Soyeon Jung, Liam Kruse, Ransalu Senanayake, Mykel Kochenderfer

While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering.

Autonomous Vehicles

3D Radar Velocity Maps for Uncertain Dynamic Environments

1 code implementation23 Jul 2021 Ransalu Senanayake, Kyle Beltran Hatch, Jason Zheng, Mykel J. Kochenderfer

This paper explores a Bayesian approach that captures our uncertainty in the map given training data.

Out-of-Distribution Detection for Automotive Perception

no code implementations3 Nov 2020 Julia Nitsch, Masha Itkina, Ransalu Senanayake, Juan Nieto, Max Schmidt, Roland Siegwart, Mykel J. Kochenderfer, Cesar Cadena

A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode.

Autonomous Driving Object Recognition +1

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders

1 code implementation NeurIPS 2020 Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone

Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction, and visual scene representation.

Image Generation Motion Planning +1

Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments

no code implementations1 Jul 2020 Ransalu Senanayake, Maneekwan Toyungyernsub, Mingyu Wang, Mykel J. Kochenderfer, Mac Schwager

We can use driving data collected over a long period of time to extract rich information about how vehicles behave in different areas of the roads.

Motion Estimation

Online Domain Adaptation for Occupancy Mapping

1 code implementation1 Jul 2020 Anthony Tompkins, Ransalu Senanayake, Fabio Ramos

Further, with the use of high-fidelity driving simulators and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy maps can be automatically adapted to accord with local spatial changes.

Autonomous Driving Domain Adaptation

Online Parameter Estimation for Human Driver Behavior Prediction

no code implementations6 May 2020 Raunak Bhattacharyya, Ransalu Senanayake, Kyle Brown, Mykel Kochenderfer

In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories.

Autonomous Vehicles

Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment

no code implementations4 Dec 2019 Vitor Guizilini, Ransalu Senanayake, Fabio Ramos

This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels.

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