Reviewerly/exHarmony huggingface dataset:
Semantic-enhanced Graph Voxelization for Pillar-based 3D Detection from Point Clouds

Pillar-based 3D detection simplifies the processing pipelines of feature extraction for irregular point clouds. However, the rigid voxelization adopted by pillar-based methods, inevitably and irreversibly, cuts off the direct connection between plenty of point pairs, thus hindering the modeling of local structures. Besides, it is difficult to handle intractable situations in 3D detection using structural information alone. In this paper, we propose a semantic-enhanced graph voxelization method to improve pillar-based feature extraction from two aspects, i.e., the construction of pillars and the learning of pillar-level features. For pillar construction, a novel voxelization method is proposed to capture more sufficient local structures with graph-based modeling of the relationships between adjacent point pairs. To learn representative features for each pillar, semantic enhancement is adopted by learning context awareness and class awareness pixel by pixel with a front-view-based segmentation branch. Extensive experiments conducted on the KITTI dataset demonstrate the effectiveness of the proposed method. Compared with the baseline, our method achieves 2.81 points and 2.65 points higher performance in 3D detection on KITTI test set for cars and cyclists individually.
A real-time semantic segmentation method for autonomous driving in surface mine

Semantic segmentation for perception of autonomous vehicles has made great progress with the development of deep learning technique. However, existing related studies mainly focus on urban scenes, thus semantic segmentation for perception of autonomous mining trucks in surface mine remains to be further explored. Considering the importance of dataset in deep learning technique, we first built a semantic segmentation dataset of surface mine. In surface mine environment, it is a fact that the particularity of the dataset brings tremendous challenge for semantic segmentation, for example, the scale variation of objects and long-tail distribution problems. So, we propose a lightweight semantic segmentation framework which consists of a multi-branch feature extraction structure and an adaptive feature fusion module. In addition, we adopt auxiliary supervision strategy to mitigate the side effect of long-tail distribution problem by mining hard samples. Experiments on our dataset show outperforming result compared with existing state-of-the-art lightweight semantic segmentation methods.
Outlier-robust Inverse Reinforcement Learning and Reward-based Detection of Anomalous Driving Behaviors

This paper considers an outlier detection problem for a collection of vehicles or agents. These agents are represented by Markov decision processes and the trajectory data are assumed available. The work aims to learn the intentions or reward functions of agents, and infer the anomalous agents whose intentions differ from the majority. To achieve this, we propose a joint inverse reinforcement learning framework, which enables learning of a common reward function that captures the behavior of the majority as well as individual rewards for normal and abnormal agents. An example on the detection and analysis of driving behaviors is provided, demonstrating the effectiveness of the proposed framework.
Semi-Supervised Image-to-Image Translation for Lane Detection in Rain

Impressive progress has recently been made in deep learning based lane detection for the autonomous vehicle domain using an in-car camera. However, relatively little attention was paid to lane detection under bad weather conditions. The general difficulty stems from the water on the road or raindrops remaining on the windscreen and hampering lane detectability. In this paper, we propose a lane enhancement approach to improve lane detection accuracy under rain. We formulate image enhancement as an image-to-image translation problem, and devise semi-supervised techniques to efficiently learn from an image set containing images from source domain (rain images) and target domain (clear images). Our semi-supervision approach differs from the conventional unsupervised image-to-image translation, in that a small amount of labelled rain images are added to the target domain in order to guide the translation to focus on enhancing the lanes while preserving the background. Specifically, we first compute the road regions in an image using vanishing points from camera intrinsic matrix. We then define a loss function using the road regions as constrains, in order to enforce lane-aware image generation. As a result, new rain images are generated by highlighting the lanes explicitly in thick bright lines. Our empirical results show that using only a few labelled images, our proposed semi-supervised learning is able to enhance lanes efficiently and improving lane detection significantly.
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset

The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention prediction models neglect the distraction state and intention of the driver, which can significantly influence how they observe their surroundings. To address these issues, we present a new driver attention dataset, CoCAtt (Cognitive-Conditioned Attention). Unlike previous driver attention datasets, CoCAtt includes perframe annotations that describe the distraction state and intention of the driver. In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions. Our results demonstrate that incorporating the above two driver states into attention modeling can improve the performance of driver attention prediction. To the best of our knowledge, this work is the first to provide autopilot attention data. Furthermore, CoCAtt is currently the largest and the most diverse driver attention dataset in terms of autonomy levels, eye tracker resolutions, and driving scenarios. CoCAtt is available for download at this link.
Environment-aware Optimization of Track-to-Track Fusion for Collective Perception

Correct and complete perception is key for autonomous vehicles to plan safe maneuvers. Especially under harsh weather conditions the use of sensing capabilities from other road users via vehicle-to-everything (V2X) communication can contribute to more complete perception. However, information from other road users may contain additional uncertainties and lead to less accurate perception. Additionally, attackers may use the V2X channel to transmit malicious data. For building an accurate environmental model an autonomous vehicle needs as precise information as possible. To tackle the problems of additional uncertainties within collective perception we propose a methodology to check perceived information for its trustworthiness and validity. This is achieved by evaluating the perception capabilities of a holistic perception pipeline and checking collectively transmitted information for consistency. The proposed approach is evaluated under varying environmental conditions on a simulated highway scenario.
Informed sampling-based trajectory planner for automated driving in dynamic urban environments

The urban environment is amongst the most difficult domains for autonomous vehicles. The vehicle must be able to plan a safe route on challenging road layouts, in the presence of various dynamic traffic participants such as vehicles, cyclists and pedestrians and in various environmental conditions. The challenge remains to have motion planners that are computationally fast and that account for future movements of other road users proactively. This paper describes an computationally efficient sampling-based trajectory planner for safe and comfortable driving in urban environments. The planner improves the Stable-Sparse-RRT algorithm by adding initial exploration branches to the search tree based on road layout information and reiterating the previous solution. Furthermore, the trajectory planner accounts for the predicted motion of other traffic participants to allow for safe driving in urban traffic. Simulation studies show that the planner is capable of planning collision-free, comfortable trajectories in several urban traffic scenarios. Adding the domain-knowledge-based exploration branches increases the efficiency of exploration of highly interesting areas, thereby increasing the overall planning performance.
ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios

Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the representations that are invariant to distortions – cross-view prediction. Augmentation is one of the key components in cross-view self-supervised learning frameworks to learn visual representations. This paper presents ExAgt, a novel method to include expert knowledge for augmenting traffic scenarios, to improve the learnt representations without any human annotation. The expert-guided augmentations are generated in an automated fashion based on the infrastructure, the interactions between the EGO and the traffic participants and an ideal sensor model. The ExAgt method is applied in two state-of-the-art cross-view prediction methods and the representations learnt are tested in downstream tasks like classification and clustering. Results show that the ExAgt method improves representation learning compared to using only standard augmentations and it provides a better representation space stability. The code is available at https://github.com/lab176344/ExAgt.
Improved RepVGG-based Anchor-free Algorithm for On-road Object Detection

On-road object detection is an important part of driverless technology. The on-road object detection task requires both detection speed and accuracy. We propose an improved RepVGG-based anchor-free real-time object detection algorithm to meet these requirements. The RepVggmodule is improved by a reparameterization method, and an adaptive Fusion-Distribution Feature Pyramid Network(FDFPN) structure is proposed, based on which an anchor-free object detection head with fewer hyperparameters is constructed to balance accuracy and speed. Experiments on KITTI dataset show that the accuracy of this method can reach 80.01%, and the inference latency is only 5.9ms in deployment mode.
Curvature Continuous Path Planning with Reverse Searching for Efficient and Precise Autonomous Parking

In autonomous driving, vehicles usually need to complete parking tasks. The existing parking planning algorithms based on hybrid A* can ensure the smoothness of the planned path, but it is inefficient due to the consumption of time. In addition, these algorithms do not fully consider the particularity of the parking scene, that is, the unbalanced distribution of obstacles around the lane and parking slot. To solve the above problems, we propose a curvature continuous parking planning method with reverse searching based on hybrid A*, called RS-HA*, which detects the collision of dense obstacles around the parking slot to prevent useless expansion nodes and complete the pruning of the algorithm to improve the real-time performance of the algorithm. We call this processing OCP (Obstacle-Collision-Pruning). RS-HA* can improve the planning efficiency and ensure the precision of the goal pose without reducing the smoothness of the path. We verified RS- HA* performance through simulation environment and real scene. The experimental results show that compared to the existing methods, RS-HA* greatly improves the parking efficiency and accuracy.
Safe Perception - A Hierarchical Monitor Approach

Our transportation world is rapidly transforming induced by an ever increasing level of autonomy. However, to obtain license of fully automated vehicles for widespread public use, it is necessary to assure safety of the entire system, which is still a challenge. This holds in particular for AI-based perception systems that have to handle a diversity of environmental conditions and road users, and at the same time should robustly detect all safety relevant objects (i.e no detection misses should occur). Yet, limited training and validation data make a proof of fault-free operation hardly achievable, as the perception system might be exposed to new, yet unknown objects or conditions on public roads. Hence, new safety approaches for AI-based perception systems are required. For this reason we propose in this paper a novel hierarchical monitoring approach that is able to validate the object list from a primary perception system, can reliably detect detection misses, and at the same time has a very low false alarm rate.
A NEAT Approach to Wave Generation in Tower Defense Games

Neural networks have shown promise when applied to video games and have proven effective at performing tasks such as dynamic difficulty adjustment (DDA). This paper explores how an evolving neural network can be applied to a tower defense game in order to generate dynamic content with the intent of increasing player engagement through the principals of flow. A NeuroEvolution of Augmenting Topologies (NEAT) neural network (NN) was trained as a wave manager to observe the current game state and generate an opposing enemy wave which best challenges the players' current tower defenses. The resulting network was compared against manually designed human waves in a blind A/B test using the Games Experience Questionnaire (GEQ) to evaluate the waves across a range of criteria. The results show that an approach like this could be viable if used for content generation purposes as no discernible difference existed in reported player experience between AI and human designed waves. However, the findings regarding subsequent increases to player engagement were inconclusive. More research is required in this field to conclusively determine if machine learning generated content can exceed the quality of content created by human designers, but the findings of this paper indicate that this approach may prove valuable to game developers in the near future by allowing them to save time and money by having AI generate content instead of requiring costly human game designer time.
Detecting and Handling Flawed Input Data in Modified Morphological Analysis Method

The techniques and procedures for handling uncertainties, namely incompleteness and contradictoriness, in input information for modified morphological analysis method are proposed and discussed. Incompleteness is considered both for input estimates of the alternatives, and for a set of alternatives of a given parameter. An approach for detecting parameters having a potentially incomplete set of alternatives is proposed, as well as a technique for assigning an estimate to auxiliary filler alternatives designed for fixing this gaps. A procedure for assessing contradictoriness of cross-consistency matrix values is also introduced and tested on an example of a previous morphological study.
Analysis of color image compression by BPG coder

Lossy image compression is a popular way to get higher compression ratio, it also has several peculiarities if one deals with compressing images corrupted by noise. First, a specific noise filtering effect is observed. Second, optimal operational point (OOP) might exist where quality of a compressed image is closer to the corresponding noise-free image according to a chosen quality metric. In this case, it is worth compressing this image in OOP area. These peculiarities have been earlier mainly studied for grayscale images. In this paper, we analyze compression of color images corrupted by additive white Gaussian noise using better portable graphics (BPG) encoder in the cases of different chroma subsampling. Based on simulation results obtained for a set of color images, the initial recommendations on encoder parameter setting are given.
Structural and Parametric Identification of Mathematical Models of Control Objects Based on the Principle of Rational Complication

The multi-step process of structural-parametric identification of mathematical models of control objects according to the principle of rational complication is considered. Identification specifies the mathematical model of the object and methods of its identification in the process of accumulation of a posteriori information. The quality of multi-step structural and parametric identification of an object depends on the amount of a priori information that grows after conducting experimental studies. For a qualitative solution to the problem, it is necessary to rationally complicate identification methods after analyzing the results of the previous identification step. Examples of application of the proposed principle of rational complication are given, which show its advantages in comparison with classical methods.
Blockchain Security for 5G Network using Internet of Things Devices

Network of vehicles using Internet of Things (IoT) frameworks have efficient characteristics of modern intelligent transportation system with a few challenges in vehicular ad-hoc networks (VANETs).However, its security framework is required to manage trust management by preserving user privacy.Wireless mobile communication ( 5G ) system is regarded as an outstanding technology that provide ultra-reliable with limited latency wireless communication services.By extension, integrating Software Defined Network (SDN) with 5G-VANET enhances global information gathering and network control.Therefore, real-time IoT application for monitoring transport services is efficiently supported.These ensures vehicular security on this framework.This paper provides a technical solution to a self-confidential framework for a smart transport system.This process exploiting IoT for vehicle communication by incorporating SDN and 5G technology.Due to some ▻
SIMR: Single Instruction Multiple Request Processing for Energy-Efficient Data Center Microservices

Contemporary data center servers process thousands of similar, independent requests per minute. In the interest of programmer productivity and ease of scaling, workloads in data centers have shifted from single monolithic processes toward a micro and nanoservice software architecture. As a result, single servers are now packed with many threads executing the same, relatively small task on different data.State-of-the-art data centers run these microservices on multi-core CPUs. However, the flexibility offered by traditional CPUs comes at an energy-efficiency cost. The Multiple Instruction Multiple Data execution model misses opportunities to aggregate the similarity in contemporary microservices. We observe that the Single Instruction Multiple Thread execution model, employed by GPUs, provides better thread scaling and has the potential to reduce frontend and memory system energy consumption. However, contemporary GPUs are ill-suited for the latency-sensitive microservice space.To exploit the similarity in contemporary microservices, while maintaining acceptable latency, we propose the Request Processing Unit (RPU). The RPU combines elements of out-of-order CPUs with lockstep thread aggregation mechanisms found in GPUs to execute microservices in a Single Instruction Multiple Request (SIMR) fashion. To complement the RPU, we also propose a SIMR-aware software stack that uses novel mechanisms to batch requests based on their predicted control-flow, split batches based on predicted latency divergence and map per-request memory allocations to maximize coalescing opportunities. Our resulting RPU system processes 5. 7 × more requests/joule than multi-core CPUs, while increasing single thread latency by only 1. ×.
Researching and Implementing the Posture Recognition Algorithm of the Elderly on Jetson Nano

Falls are a common phenomenon among the elderly. Falling not only causes serious physiological injuries such as fractures, head injuries, etc., but also causes psychological damage to the elderly. In addition to prevention, detecting falls in a timely manner can help limit the consequences of falls. In this paper, we present a fall detection method for the elderly using a neural network on Jetson Nano. The fall recognition model is built based on the Convolutional Neural Network (CNN) deep learning model. The model has functions like object body shape recognition, body recognition, and integrates a trained OpenPose algorithm model that allows receiving human body parts from which allows to predict object behavior through a Feed-Forward Networks (FFN). The experimental results on the real data set collected by us show that the proposed model is suitable for detecting falls in the elderly with an accuracy of 89.07% and the frame per second (FPS) on the Jetson Nano is 2.49.
Generative Opinion Triplet Extraction Using Pretrained Language Model

Previous researches on opinion triplet extraction for Indonesian reviews in the hotel domain has been conducted in a discriminative manner using a sequence labeling approach. However, the opinion triplet extracted by those research is limited to explicit opinion triplet only, while neglecting the opinion triplets that contains implicit aspects. In this paper, we build a model that can perform opinion triplet extraction with explicit and implicit aspects based on the seq2seq approach. The system can extract opinion triplets using pre-trained language models that have been fine-tuned on datasets with label in extraction-style paradigm. The generated text then can be extracted to get the opinion triplets. By transforming opinion triplet extraction into a text generation problem with the help of a pre-trained language model (IndoT5), we are able to improve the F1 score by 4% when compared to the findings of earlier studies.
Vehicle Distance Estimation Based on Stereo Camera System with Implementation on a Real ADAS Board

In Advanced Driver-Assistance Systems (ADAS) one of the main tasks is to determine the distance of the objects in the vehicle's surroundings. This paper presents a solution that determines the distance of a detected vehicle using a stereo image pair, which is implemented on a real ADAS embedded platform (ADAS board). To improve the performance of the solution, after disparity map calculation, the presented solution implements Fast Global Smoothing (FGS) filter with guidance over the disparity map, which smooths disparity map surfaces that represent the same object of the scene, while preserving its edges. This solution is tested in a real environment using the real ADAS board and specific automotive cameras. Distinct vehicles located at different distances from the cameras are used in tests. The results show the proposed solution can provide high-performance results when implemented on a real ADAS platform while processing 7 frames per second for videos with a resolution of 1280×360 pixels. Furthermore, it is possible to conclude that despite the somewhat slower execution of the solution using the FGS filter with guidance, it makes sense to use it because, with still acceptable execution frequency, it provides a much better distance estimation, which is extremely important for ADAS.