Deep Prediction for Self-Driving Cars

This repository contains the most recent work for the MSCV Capstone Project Deep Prediction for Self-Driving Cars. We explore several different models and optimizers on the recently popular Argoverse data.

Installation

Most of the work is done on Pytorch 1.2.0 (latest stable release, effective 09/19/2019) and using the argoverse-api.

To replicate the environment, run

conda env create -f environment.yml

This creates the environment, which can be started using,

conda activate deep_predict_argo

Then to install the argoverse-api, follow the instructions given here. We have added our own code to api to enable several missing features for our modelling.

Argoverse Extra Features

Nitin to fill this in .....

Models

• LSTM Baseline (w/ XY and Centerline data)
• Social-LSTM (with universal pooling module)
• TCN Baseline
• TrellisNet Baseline
• Transformer DEQ Baseline
• Stochastic-TCN (in development)

Execution

To train the models, run

python train_modified.py --model <model_name> --mode 'train'

To train the models, run

python train_modified.py --model <model_name> --mode 'validate'

Look at train_modified.py on how to pass model_name argument.

Optimizers

These are some recently introduced optimizers introduced (NIPS / ICCV / ICLR 2019). We have implemented the code for them but have not used them in the modelling currently.

• LookAhead Optimizer
• RectifiedAdam Optimizer [being used for TrellisNet and Transformer training]
• Ranger (RAdam + LookAhead)
• Ralamb (RAdam + LARS + LookAhead)