Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture (1509.05016v1)

Abstract: Anticipating the future actions of a human is a widely studied problem in robotics that requires spatio-temporal reasoning. In this work we propose a deep learning approach for anticipation in sensory-rich robotics applications. We introduce a sensory-fusion architecture which jointly learns to anticipate and fuse information from multiple sensory streams. Our architecture consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to capture long temporal dependencies. We train our architecture in a sequence-to-sequence prediction manner, and it explicitly learns to predict the future given only a partial temporal context. We further introduce a novel loss layer for anticipation which prevents over-fitting and encourages early anticipation. We use our architecture to anticipate driving maneuvers several seconds before they happen on a natural driving data set of 1180 miles. The context for maneuver anticipation comes from multiple sensors installed on the vehicle. Our approach shows significant improvement over the state-of-the-art in maneuver anticipation by increasing the precision from 77.4% to 90.5% and recall from 71.2% to 87.4%.

• The paper introduces a sensory-fusion architecture utilizing RNNs with LSTM units to anticipate human driving maneuvers by integrating diverse data streams.
• The proposed model achieved significant performance improvements, increasing precision from 77.4% to 90.5% and recall from 71.2% to 87.4% on a large natural driving dataset.
• This research enables advanced driver assistance systems for accident prevention and provides a versatile framework for real-time activity anticipation in other domains.

Recurrent Neural Networks for Driver Activity Anticipation via Sensory-Fusion Architecture

This paper explores a profound application in the domain of robotics and autonomous vehicles: anticipating human driving maneuvers. It introduces a deep learning approach that employs Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) units to tackle the anticipatory task, which demands robust spatio-temporal reasoning.

Key Contributions and Methodology

The central proposition of this work is a sensory-fusion architecture, which skillfully integrates multiple streams of data to anticipate future maneuvers of a driver. RNNs with LSTM units are pivotal in this architecture, as they facilitate capturing significant temporal dependencies that are crucial for anticipation tasks, addressing the known challenge of vanishing gradients that typically plague standard RNNs.

The architecture processes diverse sensory inputs; it learns to predict driving maneuvers, not only to enhance the efficacy of anticipatory actions but also to improve the interactions between different data modalities such as visual, vehicular dynamics, and GPS data. A notable feature is the sequence-to-sequence prediction training strategy, which equips the model to predict upcoming events with partial information. Additionally, an innovative loss layer is introduced, designed to prevent overfitting and promote early anticipation by scaling penalty in relation to the temporal context.

Experimental Validation and Results

The paper presents comprehensive experiments conducted on a significant dataset comprising 1180 miles of natural driving data. This dataset is well-characterized by multiple drivers across varying scenarios, cohering to the complexities encountered in real-world conditions.

Remarkably, the proposed model demonstrates significant improvements over existing methods. Precision is elevated from 77.4% to 90.5% and recall from 71.2% to 87.4%, substantiating the superior performance of the RNN-LSTM architecture over prior models like the AIO-HMM. Moreover, the model's ability to handle long temporal dependencies and the enhanced feature extraction from vision pipelines notably contribute to its success.

Implications and Future Prospects

The implications of this research are substantial. Practically, it facilitates advanced driver assistance systems that can potentially avert road accidents by providing preemptive alerts for hazardous maneuvers. Theoretically, it presents advancements in the integration of deep learning models with symbolic spatio-temporal reasoning. The architecture’s capability to handle diverse sensory data streams and temporal dependencies delivers a versatile framework applicable in other domains requiring real-time activity anticipation.

Looking forward, the integration of more complex sensory inputs, including those yielded by emerging technologies like Lidar and V2X communications, could further refine anticipation capabilities. Additionally, extending these methods for fully autonomous systems poses an intriguing trajectory in the development of anticipatory algorithms.

In conclusion, this paper marks a significant step forward in sensory-fusion architectures for activity anticipation. It opens up new avenues for research in developing anticipatory frameworks that leverage the robust, temporal modeling capabilities of RNNs with LSTM units, applicable across various autonomous and assistive technologies.