A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN

TL;DR

This paper introduces a neural network model combining Bi-LSTM and Multi-Scale CNN to automatically recognize driving behaviors from trajectory data, improving feature extraction for autonomous driving decision-making.

Contribution

The paper presents a novel trajectory-based driving behavior recognition model that integrates MSCNN with Bi-LSTM for automatic high-level feature extraction, surpassing hand-crafted methods.

Findings

Achieved high accuracy on the BLVD dataset.

Effectively captures spatial and temporal features.

Outperforms existing trajectory-based recognition methods.

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 hand-crafted 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.