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Vote-Based-EVT

The official code for the repo: Open-set Recognition for the Detection of Unknown Traffic Scenarios Based on the Combination of Convolutional Neural Networks and Random Forests from Intelligent Vehicles 2021

Dependencies

  • Python (>=3.6)
  • scikit-learn (0.21.0)
  • scipy (1.5.2)
  • numpy (0.19.0)
  • Matlab 2020a
  • Tensorflow (2.1)

Table of Contents

  1. General descriptiopn
  2. Traffic Scenarios
  3. Vote-Based EVT

General description

The classes are divided into known and unknown classes. The known classes are chosen randomly and the process is repeated 5 times. The Macro F-Score of the known classes and the unknown class is calculated for the 5 different known class sets. The CNN trained in a supervised fashion for feature extraction and is used for feature extraction. Followed by which the Voter-Based EVT Model is trained and unknown classes are detected

Traffic Scenarios

The traffic scenarios are generated from the HighD Dataset [1]. 7 common highway scenarios are extracted from highD dataset. The 7 scenarios are as follows:

- Ego - Following: The ego vehicle follows a leader vehicle.
- Ego - Right lane change: The ego makes a lane change to the right lane.
- Ego - Left lane change: The ego makes a lane change to the left lane.
- Leader - Cutin from left: The leader vehicle  makes a lane change in front of the ego lane from the left lane of ego. 
- Leader - Cutin from right: The leader vehicle  makes a lane change in front of the ego lane from the right lane of ego.
- Leader - Cutout to left: The leader vehicle  makes a lane change from  the ego lane to the left lane of ego.
- Leader - Cutout to right: The leader vehicle  makes a lane change  from  the ego lane from the right lane of ego.

plot

OpenTraffic Dataset

The *.mat file for the outlier addition experiment dataset will soon be uploaded!

Vote-Based EVT

Please fill in the forms to request access to the HighD Data from https://www.highd-dataset.com/.

Step 1: Generate scenario categories using the script \Traffic_Scenarios\highD_generate_scenarios.m, occupancy grids will be generated for the scenarios and saved for CNN+RF training

Step 2: Train the CNN and extract features for traffic scenarios using python \Traffic_Scenarios\RF+EVT\ScenarioBasic.py, followed by that to train the vote based model run the script \Traffic_Scenarios\RF+EVT\Vote_Based_EVT.m

Step 3: Evaulation can be done by using the script \Traffic_Scenarios\RF+EVT\VoteBasedEVTStat.py

Reference

[1] The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems, Krajewski et al., ITSC 2018