Name
Abstract | ||
|---|---|---|
In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision. In this paper, we propose a neural network model based on trajectories information for driving behavior recognition. Unlike existing trajectory-based methods that recognize the driving behavior using the handcrafted features or directly encoding the trajectory, our model involves a Multi-Scale Convolutional Neural Network (MSCNN) module to automatically extract the high-level features which are supposed to encode the rich spatial and temporal information. Given a trajectory sequence of an agent as the input, firstly, the Bi-directional Long Short Term Memory (Bi-LSTM) module and the MSCNN module respectively process the input, generating two features, and then the two features are fused to classify the behavior of the agent. We evaluate the proposed model on the public BLVD dataset, achieving a satisfying performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IV47402.2020.9304772 | 2020 IEEE Intelligent Vehicles Symposium (IV) |
Keywords | DocType | ISSN |
multiscale convolutional neural network,agent trajectory sequence,rich spatial information,temporal information,direct trajectory encoding,Bi-LSTM,bidirectional long short term memory,high-level features,handcrafted features,trajectory-based methods,driving behavior recognition,trajectories information,neural network model,surrounding agents,autonomous driving,multiscale CNN | Conference | 1931-0587 |
ISBN | Citations | PageRank |
978-1-7281-6674-2 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| He Zhang | 1 | 0 | 0.34 |
| Zhixiong Nan | 2 | 5 | 4.51 |
| Tao Yang | 3 | 160 | 76.32 |
| Yifan Liu | 4 | 0 | 0.34 |
| Nanning Zheng | 5 | 3975 | 329.18 |