Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention

Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demonstrated how deep learning methods can be combined to improve the segmentation and perception performance of road scene understanding systems. This paper proposes a novel segmentation system that uses fully connected networks, attention mechanisms, and multiple-input data stream fusion to improve segmentation performance. Results show comparable performance compared to previous works, with a mean intersection over union of 87.4% on the Cityscapes dataset.

Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network

Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.

Computer Vision Applications in Intelligent Transportation Systems: A Survey

As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

End-to-End Learning with Memory Models for Complex Autonomous Driving Tasks in Indoor Environments

The interest in autonomous vehicles has increased exponentially in recent years. While Lidar is a proven autonomous driving technology, end-to-end learning approaches have become popular as computer performance has improved. A fully end-to-end method—NVIDIA’s PilotNet has shown its ability to predict speed and steering angle with only camera images. This method achieved the Lidar-based methods’ performance in simple driving tasks. However, a significant drawback was no past spatiotemporal information, imposing an error-sensitive performance, especially in complex driving tasks. Spurred by this deficiency, this paper introduces two novel models: CNN + LSTM and CNN3D, aiming for complex autonomous driving tasks in indoor environments.

A High-Speed Low-Cost VLSI System Capable of On-Chip Online Learning for Dynamic Vision Sensor Data Classification

This paper proposes a high-speed low-cost VLSI system capable of on-chip online learning for classifying address-event representation (AER) streams from dynamic vision sensor (DVS) retina chips. The proposed system executes a lightweight statistic algorithm based on simple binary features extracted from AER streams and a Random Ferns classifier to classify these features. The proposed system’s characteristics of multi-level pipelines and parallel processing circuits achieves a high throughput up to 1 spike event per clock cycle for AER data processing. Thanks to the nature of the lightweight algorithm, our hardware system is realized in a low-cost memory-centric paradigm. In addition, the system is capable of on-chip online learning to flexibly adapt to different in-situ application scenarios. The extra overheads for on-chip learning in terms of time and resource consumption are quite low, as the training procedure of the Random Ferns is quite simple, requiring few auxiliary learning circuits. An FPGA prototype of the proposed VLSI system was implemented with 9.5~96.7% memory consumption and <11% computational and logic resources on a Xilinx Zynq-7045 chip platform. It was running at a clock frequency of 100 MHz and achieved a peak processing throughput up to 100 Meps (Mega events per second), with an estimated power consumption of 690 mW leading to a high energy efficiency of 145 Meps/W or 145 event/μJ. We tested the prototype system on MNIST-DVS, Poker-DVS, and Posture-DVS datasets, and obtained classification accuracies of 77.9%, 99.4% and 99.3%, respectively. Compared to prior works, our VLSI system achieves higher processing speeds, higher computing efficiency, comparable accuracy, and lower resource costs.