Journal of Autonomous Vehicles and Systems最新文献

Enhance Road Detection Data Processing of LiDAR Point Clouds to Specifically Identify Unmarked Gravel Rural Roads 加强对激光雷达点云的道路检测数据处理，以具体识别无标记的乡村砾石路

Journal of Autonomous Vehicles and Systems Pub Date : 2024-08-09 DOI: 10.1115/1.4066189

Rhett Huston, Jay Wilhelm

{"title":"Enhance Road Detection Data Processing of LiDAR Point Clouds to Specifically Identify Unmarked Gravel Rural Roads","authors":"Rhett Huston, Jay Wilhelm","doi":"10.1115/1.4066189","DOIUrl":"https://doi.org/10.1115/1.4066189","url":null,"abstract":"\u0000 Gravel roads lack standardized features such as curbs or painted lines, presenting detection challenges to autonomous vehicles. Global Positioning Service (GPS) and high resolution maps may not be reliable for navigation of gravel roads, as some roads may only be width of the vehicle and GPS may not be accurate enough. Normal Distribution Transform (NDT) LiDAR scan matching may be insufficient for navigating on gravel roads as there may not be enough geometrically distinct features for reliable scan matching. This paper examined a method of classifying scanning LiDAR spatial and remission data features for explicit detection of unmarked gravel road surfaces. Exploration of terrain classification using high resolution scanning LiDAR data of specific road surfaces may allow for predicting gravel road boundary locations potentially enabling confident autonomous operations on gravel roads. The principal outcome of this work was a method for gravel road terrain detection using LiDAR data for the purpose of predicting potential road boundary locations. Random Decision Forests were trained using scanning LiDAR data terrain classification to detect unmarked gravel and asphalt surfaces. It was found that a true-positive accuracy for gravel and asphalt surfaces was 75% and 87% respectively at an estimated rate of 13 ms per 360 degree scan. Overlapping results between manually projected and actual road surface areas resulted in 93% intersecting gravel road detection accuracy. Automated post-process examination of classification results yielded an true-positive gravel road detection rate of 72%.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"82 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tracking Algorithm Application Integrating Visual and Radar Information in Intelligent Vehicle Target Tracking 集成视觉和雷达信息的跟踪算法在智能车辆目标跟踪中的应用

Journal of Autonomous Vehicles and Systems Pub Date : 2024-08-09 DOI: 10.1115/1.4066188

Yu Wang, Jianfei Shi, Yu Zhao

{"title":"Tracking Algorithm Application Integrating Visual and Radar Information in Intelligent Vehicle Target Tracking","authors":"Yu Wang, Jianfei Shi, Yu Zhao","doi":"10.1115/1.4066188","DOIUrl":"https://doi.org/10.1115/1.4066188","url":null,"abstract":"\u0000 With the continuous development of various automobile technologies, the concept of intelligent automobile automatic driving has been introduced into people's lives, and it has great research value in traffic safety, traffic efficiency, and other aspects. Intelligent vehicles can accurately identify and track the target vehicle, which is one of the important preconditions for safe driving. However, a single tracking algorithm is often used in traditional intelligent vehicles with a low tracking accuracy under adverse circumstances. To solve this problem, a fusion tracking algorithm combining visual tracking and radar tracking algorithm is proposed, and intelligent vehicle target tracking technology is constructed based on the fusion algorithm. Through the performance comparison test, it was found that the fusion algorithm proposed in the study had the highest accuracy of 93% and the highest F measure of 0.98, both of which were superior to the comparison algorithm. Then, an empirical analysis is made of the target tracking technology proposed in the study. The results showed that the error range of yaw angle velocity of the target vehicle was −0.48 to 0.36, and the maximum root-mean-square error of lateral and longitudinal distance of the target vehicle detected by the technology was 0.03, which was superior to other tracking technologies. To sum up, the intelligent vehicle target tracking technology proposed in the research can improve the accuracy of intelligent vehicle target tracking and provide a guarantee for the safe driving of intelligent vehicles.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"36 31","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation Study on Hydraulic Braking Control of Engine Motor of Hybrid Electric Vehicle 混合动力电动汽车发动机电机液压制动控制仿真研究

Journal of Autonomous Vehicles and Systems Pub Date : 2024-07-12 DOI: 10.1115/1.4065936

Fan Kang, Min Qiao

{"title":"Simulation Study on Hydraulic Braking Control of Engine Motor of Hybrid Electric Vehicle","authors":"Fan Kang, Min Qiao","doi":"10.1115/1.4065936","DOIUrl":"https://doi.org/10.1115/1.4065936","url":null,"abstract":"\u0000 Taking the hybrid electric vehicle as the research object, under the premise of ensuring the braking safety, aiming at maximizing the use of motor regenerative braking force and improving the coordination performance of motor hydraulic braking, a simulation study of motor hydraulic braking control based on hybrid electric vehicle engine is proposed. According to the dynamic model and ideal braking force distribution curve of hybrid electric vehicle, combined with the common idea of electro-hydraulic compound braking force distribution, a three-layer braking control structure of hybrid electric vehicle is constructed. The management determines the braking intention through the driver's pedal action, calculates the expected torque, and the control layer obtains the target braking force distribution relationship through the logic gate limit control method based on the expected torque. According to the actual motor torque signal fed back by the executive layer and the wheel cylinder pressure signal of the hydraulic braking system, the braking force and regenerative braking force of the hydraulic system are dynamically coordinated and controlled to ensure that the state switching of each component can be rapid, stable and timely, and the control instruction is transmitted to the motor hydraulic braking system of the executive layer through the vehicle controller to complete the motor hydraulic braking of the hybrid electric vehicle engine. The experimental results show that this method can realize the reasonable distribution of motor hydraulic braking under different braking intensity, different initial braking speed and different pedal dip amplitude, which makes the reaction speed of hybrid electric vehicle in the braking process faster, the braking switching more stable and safe, effectively improves the energy utilization rate of hybrid electric vehicle, and ensures the economy and safety of braking control of hybrid electric vehicle.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Visual SLAM in Dynamic Environment Based on Motion Detection and Segmentation 基于运动检测和分割的动态环境中稳健的视觉 SLAM

Journal of Autonomous Vehicles and Systems Pub Date : 2024-07-02 DOI: 10.1115/1.4065873

Xin Yu, Rulin Shen, Kang Wu, Zhi Lin

引用次数: 0

Two-Carrier Cooperative Parking Robot: Design and Implementation 双载波合作停车机器人：设计与实现

Journal of Autonomous Vehicles and Systems Pub Date : 2024-06-03 DOI: 10.1115/1.4065645

Pengfei Su, Natnael Getasew Tsehay, Wei Wang, Qixiang Zhao, Yangmin Li

引用次数: 0

Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field with Biologically Inspired Interactions 在通信受限的环境中使用具有生物启发交互作用的混合领导者-追随者-人工势场的分散群控技术

Journal of Autonomous Vehicles and Systems Pub Date : 2024-05-20 DOI: 10.1115/1.4065533

Christopher T. Goodin, Lucas Cagle, Greg Henley, Brandon Black, Justin Carrillo, David P. McInnis

{"title":"Decentralized Swarm Control in Communication-Constrained Environments Using a Blended Leader Follower-Artificial Potential Field with Biologically Inspired Interactions","authors":"Christopher T. Goodin, Lucas Cagle, Greg Henley, Brandon Black, Justin Carrillo, David P. McInnis","doi":"10.1115/1.4065533","DOIUrl":"https://doi.org/10.1115/1.4065533","url":null,"abstract":"\u0000 This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader-follower / artificial potential field approach. While teams of autonomous ground vehicles (AGV) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential-field planning method with the leader-follower control algorithm and biologically-inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 meters to 200 meters, with the exact reduction also depending on the number of vehicles.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"15 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HYDROPLANING OF TIRES: A REVIEW OF NUMERICAL MODELING AND NOVEL SENSING METHODS 轮胎水滑：数值建模和新型传感方法综述

Journal of Autonomous Vehicles and Systems Pub Date : 2024-04-23 DOI: 10.1115/1.4065379

Alexandru Vilsan, Corina Sandu

{"title":"HYDROPLANING OF TIRES: A REVIEW OF NUMERICAL MODELING AND NOVEL SENSING METHODS","authors":"Alexandru Vilsan, Corina Sandu","doi":"10.1115/1.4065379","DOIUrl":"https://doi.org/10.1115/1.4065379","url":null,"abstract":"\u0000 This article represents an extensive literature on tire hydroplaning, specifically focusing on the assessment of real-time estimation methodologies and numerical modeling for both partial and total hydroplaning phenomenon. Hydroplaning still poses a significant challenge for contemporary passenger cars, even those equipped with state of the art safety systems. The active safety features that equip the most technologically advanced passenger cars are unable to forecast and prevent the occurrence of hydroplaning. Total hydroplaning represents a phenomenon which occurs when the tire reaches a point where it can no longer expel the water from its tread grooves, leading to a complete control loss of the motor vehicle. This describes a scenario in which the entire contact patch is lifted from the ground due to the hydrodynamic forces generated at the contact between the tire and the layer of water formed on the road. Nevertheless, the decrease in contact between the tire and the road surface occurs gradually, a phenomenon which is presented in literature as partial hydroplaning. The longitudinal speed that marks the transition from partial hydroplaning to total hydroplaning is defined as the critical hydroplaning speed. These principles are widely acknowledged among researchers in the hydroplaning field. Nonetheless, the literature review reveals variations for defining the critical hydroplaning speed threshold across different experimental investigations. In this article, past studies, and state-of-the-art research on tire hydroplaning has been reviewed, especially focusing on real-time estimation methodologies and numerical modeling of the partial and of the total hydroplaning phenomenon.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"34 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Application of Deep Learning-based Crash Damage Prediction Model for Self-Driving Cars 基于深度学习的自动驾驶汽车碰撞损伤预测模型的设计与应用

Journal of Autonomous Vehicles and Systems Pub Date : 2024-04-12 DOI: 10.1115/1.4065307

Wenxia Zhang, Zhixue Wang

{"title":"Design and Application of Deep Learning-based Crash Damage Prediction Model for Self-Driving Cars","authors":"Wenxia Zhang, Zhixue Wang","doi":"10.1115/1.4065307","DOIUrl":"https://doi.org/10.1115/1.4065307","url":null,"abstract":"\u0000 The collision damage of automated cars has grown in importance as self-driving car technology has advanced to the pilot operation stage. The study builds a collision damage prediction model for automated driving cars, optimized deep convolutional neural networks using the self-attention mechanism, and designs a degree convolutional neural network algorithm incorporating the attention mechanism in order to avoid the dangers that will be encountered on the way to automated driving in advance. The findings demonstrated that the four index values of the modified algorithm in the calculation of the index were, respectively, 94.0%, 94.8%, 93.6%, and 0.88, with higher overall performance. The prediction model's accuracy during training on the training data set and validation data set was 100% and 98%, respectively, demonstrating its efficacy. The prediction model's prediction accuracy in calculating the degree of auto collision damage for 10 working conditions in the validation dataset is 83.3%, and the prediction results are essentially consistent with the trend of the actual collision damage degree curve, demonstrating both the viability and high prediction accuracy of the prediction model. The aforementioned findings demonstrated the model's strong performance and great application value in the field of self-driving car collision avoidance and warning.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"55 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140709787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-modal importance sampling approach for the probabilistic safety assessment of automated driver assistance systems 自动驾驶辅助系统概率安全评估的多模式重要性抽样方法

Journal of Autonomous Vehicles and Systems Pub Date : 2024-04-12 DOI: 10.1115/1.4065308

Thomas Most, Maximillian Rasch, Paul Tobe Ubben, Roland Niemeier, Veit Bayer

引用次数: 0

A Hierarchical Temporal Memory Based End-to-End Autonomous Driving System 基于分层时态记忆的端到端自动驾驶系统

Journal of Autonomous Vehicles and Systems Pub Date : 2024-03-04 DOI: 10.1115/1.4064989

Luc Le Mero, M. Dianati, Graham Lee

{"title":"A Hierarchical Temporal Memory Based End-to-End Autonomous Driving System","authors":"Luc Le Mero, M. Dianati, Graham Lee","doi":"10.1115/1.4064989","DOIUrl":"https://doi.org/10.1115/1.4064989","url":null,"abstract":"\u0000 Achieving human-level driving performance in complex environments remains a major challenge in the field of Deep Learning (DL) based end-to-end Autonomous Driving Systems (ADS). In ADS, generalization to rare edge cases poses a serious safety concern with DL based models. The leading solution to this problem is scaling; the construction of larger models and datasets. However, limitations in the computational power available to autonomous vehicles, coupled with the under-representation of safety-critical edge cases in large autonomous driving datasets raise questions over the suitability of scaling for ADS. In this work, we investigate the performance of an alternate, computationally less demanding, Machine Learning (ML) algorithm, Hierarchical Temporal Memory (HTM). Existing HTM models use rudimentary encoding schemes that have thus far limited their application to simple inputs. Motivated by this shortcoming, we first propose a bespoke CNN based encoding scheme suited to the input data used in ADS. We then integrate this encoding scheme into a novel DL-HTM end-to-end ADS. The proposed DL-HTM based end-to-end ADS is trained and evaluated against a conventional DL end-to-end ADS based on the widely used AlexNet model from the literature. Our evaluation results show that the proposed DL-HTM model achieves comparable performance with far fewer trainable parameters than the conventional DL based end-to-end ADS. Results also indicate that the proposed model demonstrates a superior capacity for learning under-represented classes, i.e. edge cases, in the dataset.","PeriodicalId":164923,"journal":{"name":"Journal of Autonomous Vehicles and Systems","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140398230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0