Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network

By Byeoungdo Kim, Chang Mook Kang, Jaekyum Kim, Seung-Hi Lee, Chung Choo Chung, Jun Won Choi. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017

361 citations recorded by the source corpus.

In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.

Subfields: Automotive Engineering, Building and Construction, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/59e4224c1013e2fbedf2a49958f443a674348115

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Citations in the corpus

Convolutional Social Pooling for Vehicle Trajectory Prediction (2018, background): “Kim et al. [11] propose an LSTM that predicts the location of vehicles in an occupancy grid at intervals of 0.5 s, 1 s and 2 s into the future.”
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks (2018, methodology): “In [30], LSTM was applied to predict vehicle locations using past trajectory data.”
Human motion trajectory prediction: a survey (2019, methodology): “…Xiao et al. 2015; Goldhammer et al. 2014; Chen et al. 2008, 2016; Suraj et al. 2018; Habibi et al. 2018; Hermes et al. 2009; Molina et al. 2018; Kim et al. 2017; Saleh et al. 2018b; Xue et al. 2019, 2017) and planning-based methods (Yen et al. 2008; Ziebart et al. 2009; Vasquez 2016; Kitani et…”
SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints (2018, background): “Recently, Kim et al. [12], train a separate recurrent network, one for each future time step, to predict the location of nearby cars.”
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs (2018, background): “[21] propose an LSTM that predicts the location of vehicles in an occupancy grid at intervals of 0.”
It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction (2020, methodology): “Kim et al. [21] trained a RNN combining multiple Long Short-term Memory (LSTM) units to predict the location of nearby cars.”
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches (2018, positive): “Vehicle Trajectory prediction using an RNN [164].”