High Efficiency Pedestrian Crossing Prediction
- URL: http://arxiv.org/abs/2204.01862v1
- Date: Mon, 4 Apr 2022 21:37:57 GMT
- Title: High Efficiency Pedestrian Crossing Prediction
- Authors: Zhuoran Zeng
- Abstract summary: State-of-the-art methods in predicting pedestrian crossing intention often rely on multiple streams of information as inputs.
We introduce a network with only frames of pedestrians as the input.
Experiments validate that our model consistently delivers outstanding performances.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting pedestrian crossing intention is an indispensable aspect of
deploying advanced driving systems (ADS) or advanced driver-assistance systems
(ADAS) to real life. State-of-the-art methods in predicting pedestrian crossing
intention often rely on multiple streams of information as inputs, each of
which requires massive computational resources and heavy network architectures
to generate. However, such reliance limits the practical application of the
systems. In this paper, driven the the real-world demands of pedestrian
crossing intention prediction models with both high efficiency and accuracy, we
introduce a network with only frames of pedestrians as the input. Every
component in the introduced network is driven by the goal of light weight.
Specifically, we reduce the multi-source input dependency and employ light
neural networks that are tailored for mobile devices. These smaller neural
networks can fit into computer memory and can be transmitted over a computer
network more easily, thus making them more suitable for real-life deployment
and real-time prediction. To compensate the removal of the multi-source input,
we enhance the network effectiveness by adopting a multi-task learning
training, named "side task learning", to include multiple auxiliary tasks to
jointly learn the feature extractor for improved robustness. Each head handles
a specific task that potentially shares knowledge with other heads. In the
meantime, the feature extractor is shared across all tasks to ensure the
sharing of basic knowledge across all layers. The light weight but high
efficiency characteristics of our model endow it the potential of being
deployed on vehicle-based systems. Experiments validate that our model
consistently delivers outstanding performances.
Related papers
- Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
This paper introduces the SANE approach to weight-space learning.
Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
arXiv Detail & Related papers (2024-06-14T13:12:07Z) - Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for
Autonomous Real-World Reinforcement Learning [58.3994826169858]
We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning.
Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy.
Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
arXiv Detail & Related papers (2023-10-23T17:50:08Z) - Task-Attentive Transformer Architecture for Continual Learning of
Vision-and-Language Tasks Using Knowledge Distillation [18.345183818638475]
Continual learning (CL) can serve as a remedy through enabling knowledge-transfer across sequentially arriving tasks.
We develop a transformer-based CL architecture for learning bimodal vision-and-language tasks.
Our approach is scalable learning to a large number of tasks because it requires little memory and time overhead.
arXiv Detail & Related papers (2023-03-25T10:16:53Z) - Machine Learning for QoS Prediction in Vehicular Communication:
Challenges and Solution Approaches [46.52224306624461]
We consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications.
We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data.
We use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed.
arXiv Detail & Related papers (2023-02-23T12:29:20Z) - A Lightweight, Efficient and Explainable-by-Design Convolutional Neural
Network for Internet Traffic Classification [9.365794791156972]
This paper introduces a new Lightweight, Efficient and eXplainable-by-design convolutional neural network (LEXNet) for Internet traffic classification.
LEXNet relies on a new residual block (for lightweight and efficiency purposes) and prototype layer (for explainability)
Based on a commercial-grade dataset, our evaluation shows that LEXNet succeeds to maintain the same accuracy as the best performing state-of-the-art neural network.
arXiv Detail & Related papers (2022-02-11T10:21:34Z) - Routing with Self-Attention for Multimodal Capsule Networks [108.85007719132618]
We present a new multimodal capsule network that allows us to leverage the strength of capsules in the context of a multimodal learning framework.
To adapt the capsules to large-scale input data, we propose a novel routing by self-attention mechanism that selects relevant capsules.
This allows not only for robust training with noisy video data, but also to scale up the size of the capsule network compared to traditional routing methods.
arXiv Detail & Related papers (2021-12-01T19:01:26Z) - Multi-modal Experts Network for Autonomous Driving [16.587968446342995]
End-to-end learning from sensory data has shown promising results in autonomous driving.
It is challenging to train and deploy such network and at least two problems are encountered in the considered setting.
We propose a novel, carefully tailored multi-modal experts network architecture and propose a multi-stage training procedure.
arXiv Detail & Related papers (2020-09-18T14:54:54Z) - Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G
Networks [84.2155885234293]
We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC.
To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
arXiv Detail & Related papers (2020-02-22T14:38:11Z) - Firearm Detection and Segmentation Using an Ensemble of Semantic Neural
Networks [62.997667081978825]
We present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks.
A set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel.
The overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives.
arXiv Detail & Related papers (2020-02-11T13:58:16Z) - NeurAll: Towards a Unified Visual Perception Model for Automated Driving [8.49826472556323]
We propose a joint multi-task network design for learning several tasks simultaneously.
The main bottleneck in automated driving systems is the limited processing power available on deployment hardware.
arXiv Detail & Related papers (2019-02-10T12:45:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.