Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving

• URL: http://arxiv.org/abs/2001.09220v1
• Date: Fri, 24 Jan 2020 22:58:55 GMT
• Title: Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving
• Authors: Wei Wang, Shibo Zhou, Jingxi Li, Xiaohua Li, Junsong Yuan, Zhanpeng Jin
• Abstract summary: We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
• Score: 65.36115045035903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving. Even though deep neural networks (DNNs) have been successfully applied in this area, most existing methods still heavily rely on the pre-processing of the pulse signals derived from LiDAR sensors, and therefore introduce additional computational overhead and considerable latency. In this paper, we propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN). Being evaluated on various datasets (including Sim LiDAR, KITTI and DVS-barrel) derived from LiDAR and dynamic vision sensor (DVS), our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency. It highlights the SNN's great potentials in autonomous driving and related applications. To the best of our knowledge, this is the first attempt to use SNN to directly perform object recognition on raw temporal pulses.

Related papers

• Stochastic Spiking Neural Networks with First-to-Spike Coding [7.955633422160267]
  Spiking Neural Networks (SNNs) are known for their bio-plausibility and energy efficiency. In this work, we explore the merger of novel computing and information encoding schemes in SNN architectures. We investigate the tradeoffs of our proposal in terms of accuracy, inference latency, spiking sparsity, energy consumption, and datasets.
  arXiv  Detail & Related papers  (2024-04-26T22:52:23Z)
• Radar-Based Recognition of Static Hand Gestures in American Sign Language [17.021656590925005]
  This study explores the efficacy of synthetic data generated by an advanced radar ray-tracing simulator. The simulator employs an intuitive material model that can be adjusted to introduce data diversity. Despite exclusively training the NN on synthetic data, it demonstrates promising performance when put to the test with real measurement data.
  arXiv  Detail & Related papers  (2024-02-20T08:19:30Z)
• Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change Detection [0.0]
  Spiking neural networks (SNNs) provide an alternative model to process information and make decisions. Recent work using SNNs for autonomous driving mostly focused on simple tasks like lane keeping in simplified simulation environments. This project studies SNNs on photo-realistic driving scenes in the CARLA simulator, which is an important step toward using SNNs on real vehicles.
  arXiv  Detail & Related papers  (2023-09-27T23:31:30Z)
• NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving [57.03126447713602]
  We present a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors. The network runs faster than real time on an embedded GPU and shows good generalization across geographic regions.
  arXiv  Detail & Related papers  (2022-09-29T01:30:34Z)
• LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor [12.47874622269824]
  We present a new SNN-based approach, called LaneSNN, for detecting the lanes marked on the streets using the event-based camera input. We implement and map the learned SNNs models onto the Intel Loihi Neuromorphic Research Chip. For the loss function, we develop a novel method based on the linear composition of Weighted binary Cross Entropy (WCE) and Mean Squared Error (MSE) measures.
  arXiv  Detail & Related papers  (2022-08-03T14:51:15Z)
• Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware [50.380319968947035]
  Recent deep learning approaches have reached accuracy in such tasks, but their implementation on conventional embedded solutions is still computationally very and energy expensive. We propose a new benchmark for computing tactile pattern recognition at the edge through letters reading. We trained and compared feed-forward and recurrent spiking neural networks (SNNs) offline using back-propagation through time with surrogate gradients, then we deployed them on the Intel Loihimorphic chip for efficient inference. Our results show that the LSTM outperforms the recurrent SNN in terms of accuracy by 14%. However, the recurrent SNN on Loihi is 237 times more energy
  arXiv  Detail & Related papers  (2022-05-30T14:30:45Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

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.