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Stanford Intelligent Systems Laboratory's Projects

2016_itsc_probdrive icon 2016_itsc_probdrive

Model details, parameters, and associated code for traffic propagation models associated with "Analysis of Microscopic Behavior Models for Probabilistic Modeling of Driver Behavior" - ITSC 2016

2016_itsc_scenegen icon 2016_itsc_scenegen

Model details, parameters, and associated code for the scene generation models associated with "Factor Graph Scene Distributions for Automotive Safety Analysis" - ITSC 2016

aa222project0 icon aa222project0

Starter code for Project 0 of Stanford AA222/CS361 (Engineering Design Optimization)

aa222project1 icon aa222project1

Starter code for Project 1 of Stanford AA222/CS361 (Engineering Design Optimization)

aa222project2 icon aa222project2

Starter code for Project 2 of Stanford AA222/CS361 (Engineering Design Optimization) Spring 2020

aa222testing icon aa222testing

Code testing framework for AA222 Spring 2020. Can output test results in the format Gradescope expects.

aa228-notebook icon aa228-notebook

IJulia notebooks for AA228/CS238 Decision Making Under Uncertainty course at Stanford University

aa228student.jl icon aa228student.jl

Stanford University's AA228/CS238 student project workspace (Autumn 2019)

action_suggestions icon action_suggestions

A method of collaborative decision making using action suggestions by using the agent's policy to estimate the distribution over suggestions and treating a suggested action as an observation of the environment to update the agent's belief.

adpm icon adpm

Aircraft Acceptance Rate (AAR) Distribution Prediction Model, a model for predicting distributions over future AARs using weather reports and forecasts

attentionaugmentedconvlstm icon attentionaugmentedconvlstm

Implementation of TAAConvLSTM and SAAConvLSTM used in "Attention Augmented ConvLSTM for Environment Prediction"

autoatc icon autoatc

source code for the autonomous ATC for non-towered airport concept

automotivesaferl icon automotivesaferl

Training and evaluation scripts for applying formal methods and reinforcement learning to autonomous driving problems.

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