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Papers

[Lane Change] Predicting future lane changes of other highway vehicles using RNN-based deep models, arXiv, 2018

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Summary

1. the state vector that represents the current state of a car includes {Position, Heading, Speed, Yaw rate, the number of lanes to the left, the number of lanes to the right}

2. the state vector of the target as well as those of its six neighbors are used for input 

3. 4 LSTMs are used and the network structure is determined based on factor model

 

 

1. Input

. i-th vehicle's state vector includes {Position, Heading, Speed, Yaw rate, the number of lanes to the left, the number of lanes to the right}

. the observed state vectors are used for input

. the target veh. as well as its neighbors are considered

 

 

 

2. Network structure

 

. three RNNs represent three factor functions of the following equations.

 

 

. red factor function (corresponds to the upper RNN in the network structure figure) presents the relations between the target and the neighbors in the left lane

. green and blue, respectively, present the relations to the same lane and the right lane

 

 

3. Results