Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Sol...

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Kaggle Lyft Motion Prediction
4th place solution

● We are #4 out of 935 teams, in competitive situation.
Competition result:
PFN!

Competition introduction

● Kaggle: Lyft Motion Prediction for Autonomous Vehicles 
● l5kit Data HP: Data - Lyft 
Competition/Dataset page

● Focus on “Motion Prediction” part
○ Given bird-eye-view image (No natural images)
○ Predict 3 possible trajectories with...

● It was focusing “Perception” part
○ https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles
○ Detect car as...

● Information in the bird-eye-view
○ Label of passengers (e.g. car, bicycle and pedestrian...)
○ Status of traffic light
○...

● Total dataset size: 1118 hours, 26344 km
● Road length: 6.8 miles
● Train (89GB), Validation (11GB), Test Dataset (3GB):...

EDA
Exploratory Data Analysis

● Route on google map
● Not so long distance, around Lyft office (Actually, CNN can “memorize” the place from image)
EDA u...

● Many straight roads
● Some complicated intersections...
EDA using google earth

● More & more EDA, Train/Valid/Test stat is almost same!
No extrapolation found in this dataset…
○ Agent type distribution...

Technology stack

● Structured numpy array + zarr is used to save data on disk.
● structured array: https://numpy.org/doc/stable/user/basics...

● l5kit is provided as baseline: https://github.com/lyft/l5kit
○ (Complicated) data preprocessing part is already implemen...

Approach

Short Summary
● Distributed training: 8 V100 GPUs * 5 days for 1 epoch

● 1. Use train_full.zarr
● 2. l5kit==1.1.0
● 3. Set min_history=0, min_future=10 in AgentDataset
● 4. Cosine annealing for...

Solutions

● How to predict probabilistic behavior?
● Suggested Baseline kernel “Lyft: Training with multi-mode confidence”
○ Single ...

Approach/Metric:
• In this competition, model outputs 3 hypotheses (trajectories). 
– ground truth: 
– hypotheses: 
• Assu...

● To utilize all possible data? → Let’s use train_full.zarr without down sampling
○ But size is big!….
○ 89 GB
○ 191,177,8...

● torch.distributedis used
○ 8 V100 GPUs * 5 days for 1 epoch
● Practically, need to modify AgentDataset to cache index ar...

● Pointed out in “We did it all wrong” discussion:
○ The target_positions value need to be rotated in the same way with th...

● Use chopped dataset: Only use 100-th frame from each scene.
○ This is how test data is made.
○ But it discards all groun...

● Set min_history=0, min_future=10 in AgentDataset
○ MOST IMPORTANT!
○ Public LB Score jumps to 13.059 here.
Align trainin...

● Tried several models
● Worked Well:
○ Resnet18
○ Resnet50
○ SEResNeXt50
○ ecaresnet18
● Not working well: Big, deeper mo...

● Trained hyperparameters
○ Batch size 12 * 8 processes
○ Adam optimizer
○ Cosing annealing with 1 epoch (Better than Expo...

● Used albumentationslibrary, tried several augmentations.
○ Tried Cutout, Blur, Downscale
○ Other augmentation used in na...

● Modified BoxRasterizer to add augmentation
○ 1. Random Agent drop
○ 2. Agent extent size scaling
● We could not find cle...

● How to ensemble models? 
○ In this competition, we train model to predict three trajectories (x1,x2,x3) and
three confid...

● You can get ensembled outputs from by
following the steps below. 
○ Sampling enough points (e.g. 1000N) from the distrib...

model1:loss=67.85 
model2:loss=77.60 
ensemble model:loss=8.26 
Ensemble by GMM and EM algorithm
sampling from GMM  fi...

model1:loss=3340 
model2:loss=68.99 
ensemble model:loss=69.69 
Ensemble by GMM and EM algorithm
sampling from GMM  fi...

● The final best submission was ensemble of 9 different models
● That’s all for our solution presentation, thank you!
Fina...

Other approach &
Future discussion

● CNN Models: Smaller model was enough
○ ResNet18 was enough to get 4th place
○ Tried bigger ResNet101, ResNet152, etc… Bu...

● https://www.kaggle.com/c/lyft-motion-prediction-autonomous-vehicles/discussion/201493
● Optimize Rasterizer implementati...

● 10th place solution GNN based methods called VectorNet
○ Faster training & inference
■ They did not use rasterized image...

Appendix1
Data analysis/Error analysis

● How different is the 3 trajectory generated by CNN models?
● Case1: Different directions
○ CNN can predict different pos...

● How different is the 3 trajectory generated by CNN models?
● Case2: Speed or start time is different
○ Even direction is...

Appendix2
What we tried and not worked

● raster_size (Image size)
○ Tried 224x224 & 128x128.
○ Default 224x224 was better
● pixel_size
○ Tried 0.5, 0.25, 0.15.
○...

● Added velocity arrow to the BoxRasterizer
Custom Rasterizer: 1. VelocityBoxRasterizer

● Original SemanticRasterizer: Semantic image is drawn as RGB image
Custom Rasterizer: 2. ChannelSemanticRasterizer
● Chan...

● We thought that the red signal length is important to predict when the stopping
agent starts moving in the future.
● Thi...

● Draw each agent type in different color/channel
○ CAR = Blue
○ CYCLIST = Yellow
○ PEDESTRIAN = Red
○ UNKNOWN = Gray
● Un...

● Predict all agent’s future coords at once, from 1 image.
● Using semantic segmentation models (segmentation-models-pytor...

● What kind of data makes the serious big error?
● When the “yaw” annotation is wrong, prediction & actual direction becom...

● Kaggle page: Lyft Motion Prediction for Autonomous Vehicles
● Data HP: https://self-driving.lyft.com/level5/data/
● Solu...

Kaggle Lyft Motion Prediction for Autonomous Vehicles 4th Place Solution

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