DeepAI AI Chat
Log In Sign Up

MMFN: Multi-Modal-Fusion-Net for End-to-End Driving

by   Qingwen Zhang, et al.
The Hong Kong University of Science and Technology

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks. In addition, we design a new expert to further enhance the model performance by considering multi-road rules. Experimental results prove that both of the proposed improvements enable our agent to achieve superior performance compared with other methods.


Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving

We present an end-to-end method for object detection and trajectory pred...

Multi-Modal Fusion Transformer for End-to-End Autonomous Driving

How should representations from complementary sensors be integrated for ...

End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

End-to-end approaches to autonomous driving commonly rely on expert demo...

LiDAR-as-Camera for End-to-End Driving

The core task of any autonomous driving system is to transform sensory i...

MP3: A Unified Model to Map, Perceive, Predict and Plan

High-definition maps (HD maps) are a key component of most modern self-d...

Variational End-to-End Navigation and Localization

Deep learning has revolutionized the ability to learn "end-to-end" auton...

Closing the gap towards end-to-end autonomous vehicle system

Designing a driving policy for autonomous vehicles is a difficult task. ...