LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor

Viale, Alberto; Marchisio, Alberto; Martina, Maurizio; Masera, Guido; Shafique, Muhammad

doi:10.1109/iros47612.2022.9981034

Cited by 7 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking the reported performances of SNN methods based on different sensors that we illustrate in TABLE I into consideration, we can see the comparison of the proposed method with the other methods. Since there are rare state-ofart SNN models solving the lane area segmentation problem, only methods reported in [9], [12] in the segmentation purpose targeted for driving scenes are considered in comparison with our method, in which dynamic vision sensors (DVS) are used. Models proposed in [9] perform semantic segmentation on the DDD17 dataset [15] achieve 33.7% and 34.2% IoU.…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…RELATED WORK There is an increasing amount of research in the literature regarding SNN solutions for perception problems [7]- [9], [11]. However, there are a very limited number of SNN studies related to semantic segmentation that only emerge in recent years [9], [12], [13]. Kucik et al in [13] proposed an SNN model to perform space-scene classification for land cover and land use based on satellite images.…”

Section: Introductionmentioning

confidence: 99%

“…Kucik et al in [13] proposed an SNN model to perform space-scene classification for land cover and land use based on satellite images. With regard to autonomous driving, dynamic vision sensors (DVS) and cameras are mainly used in methods detailed in [9], [12]. In TABLE I, the employed sensors and performances of different SNN methods for semantic segmentation are listed for reference.…”

Section: Introductionmentioning

confidence: 99%

“…The models are only camera-centric. In publication [12] detect lane markers in the road based on DVS. However, the method is only capable of detecting lane markers.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Toward Neuromorphic Perception: Spike-driven Lane Segmentation for Autonomous Driving using LiDAR Sensor

Zhuang,

Bing,

Huang

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

As a prerequisite of high vehicle autonomy, lane segmentation is a significant perception task for advanced autonomous driving. In recent years, spiking neural networks (SNNs) have garnered the attention of researchers due to their appealing power efficiency, which provides the potential to improve energy consumption for the perception system on power-constrained autonomous vehicles. In this paper, we propose a spiking neural network targeted for LiDAR sensors to solve the lane segmentation problem. By encoding the LiDAR point cloud into spikes, the proposed SNN constructed in an end-to-end fully convolutional network structure is capable of processing the LiDAR input through the network to segment the lane area effectively. Experiments conducted on the KITTI dataset for urban scenes and the power consumption evaluation demonstrate the high performance and energy efficiency of the proposed SNN for LiDAR-based lane segmentation.

show abstract

Section: B Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The models are only camera-centric. In publication [12] detect lane markers in the road based on DVS. However, the method is only capable of detecting lane markers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Toward Neuromorphic Perception: Spike-driven Lane Segmentation for Autonomous Driving using LiDAR Sensor

Zhuang,

Bing,

Huang

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

show abstract

“…Some of the neuromorphic chips, such as IBM's TrueNorth chip [31] and Intel's Loihi chip [28], use spiking neural networks to process information in a way that is closer to how the brain processes information. These chips have been used for a wide range of applications, including image and speech recognition [32], robotics [33], and autonomous vehicles [34]. The advancement of brain-inspired hardware also provides a potential for significant breakthroughs in the field of AGI by paving the road for generalized hardware platforms [30].…”

Section: Introductionmentioning

confidence: 99%

When Brain-inspired AI Meets AGI

Zhang¹,

Zhang²,

Wu³

et al. 2023

Preprint

View full text Add to dashboard Cite

The human brain is widely considered one of the most intricate and advanced information-processing systems in the world. It comprises over 86 billion neurons, each capable of forming up to 10,000 synapses with other neurons, resulting in an exceptionally complex network of connections that allows for the proliferation of intelligence. Along with the physiological complexity, the human brain exhibits a wide range of characteristics that contribute to its remarkable functional capabilities. For example, it can integrate data from multiple sensory modalities, such as vision, hearing, and touch, allowing it to form a coherent perception of the world. The brain's ability to perform parallel processing is also essential for efficiently handling multiple information streams simultaneously. This is fulfilled via the connections and real-time communications among different brain regions, though the mechanism is not fully understood. Besides, the brain is highly adaptable, capable of reorganizing its structure and function in response to changing environments and experiences. This property, known as neuroplasticity, enables the brain to learn and develop new skills throughout life. The human brain is also notable for its high-level cognitive functions, such as problem-solving, decision-making, creativity, and abstract reasoning, supported by the prefrontal cortex, a brain region that is particularly well-developed in humans.Creating artificial general intelligence (AGI) system that has human-level or even higher intelligence and is capable of performing a wide range of intellectual tasks, such as reasoning, problem-solving, and creativity, is the pursuit of humanity for centuries, which can date back to the mid-20th century. In the 1940s, pioneers such as Alan Turing developed early ideas about computing machines and the potential for them to simulate human thinking [1]. From then on, seeking to replicate the principles of human intelligence in artificial systems has significantly promoted the development of AGI and the corresponding applications. These principles include the structure and function of neural networks, the plasticity of synaptic connections, the dynamics of neural activity, and more. In 1943, McCulloch and Pitts proposed the very first mathematical model of an artificial neuron [2], also known as McCulloch-Pitts (MCP) Neuron. Inspired by the Hebbian theory of synaptic plasticity, Frank Rosenblatt came up with the perceptron, a major improvement over the MCP neuron model [3], and showed that by relaxing some of the MCP's rules artificial neurons could actually learn from data. However, the research of artificial neural network had stagnated until the backprogation was proposed by Werbos in 1975 [4]. Backpropagation was inspired by the way the brain modifies the strengths of connections between neurons to learn and improve its performance through synaptic plasticity. Backpropagation attempts to mimic this process by adjusting the weights (synaptic strengths) between neurons in an artificial neural network. De...

show abstract

Embedded Neuromorphic Using Intel’s Loihi Processor

Marchisio,

Shafique

2023

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

View full text Add to dashboard Cite

LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor

Cited by 7 publications

References 30 publications

Toward Neuromorphic Perception: Spike-driven Lane Segmentation for Autonomous Driving using LiDAR Sensor

Toward Neuromorphic Perception: Spike-driven Lane Segmentation for Autonomous Driving using LiDAR Sensor

When Brain-inspired AI Meets AGI

Embedded Neuromorphic Using Intel’s Loihi Processor

Contact Info

Product

Resources

About