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1.
Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels. 相似文献
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《Mechatronics》2022
This paper presents an approach to modify CAD/CAM generated motion profiles for wire bending machines, in order to damp wire oscillations without decreasing machine throughput. Two different methodologies are presented, both leveraging on a simple and easily identifiable model of wire oscillations, the first one based on a filtering approach, the second one on an optimisation approach. The two methodologies are both characterised by a low computational complexity, allowing them to be integrated directly in the bending machine user interface, and can rely on a standard camera to identify wire oscillation parameters. A thorough experimental validation of the approaches is also presented, showing promising results in damping oscillations with wires of different materials. 相似文献
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The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems. 相似文献
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为应对"三软"煤层综放沿空掘巷围岩稳定性差的问题,通过数值模拟分析、揭示"三软"煤层中锚杆、锚索载荷变化规律及其与围岩变形相互作用关系,为"三软"煤层沿空掘巷锚网支护参数优化提供理论依据,改善了巷道支护状况,降低相邻工作面间保护煤柱压煤率,提高矿井经济效益和社会效益。 相似文献
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Wonje Jang Junhyuk Hyun Jhonghyun An Minho Cho Euntai Kim 《IEEE/CAA Journal of Automatica Sinica》2022,9(1):187-204
The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation. 相似文献
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《Contact lens & anterior eye》2022,45(4):101493
PurposeThe purpose of this study was to compare sign identification distances and driving performance metrics in presbyopic participants while wearing multifocal contact lenses (MFCL) and while wearing progressive addition lens (PAL) spectacles.Methods19 presbyopic participants completed PAL spectacle assessments and contact lens fitting and follow up visits before driving assessments began. These assessments occurred in a simulator equipped with a full-sized sedan on a motion platform and a 260 degree screen. Participants completed the driving task with PAL and with MFCL. Participants followed a lead car and identified signs at various distances from the road. For the two wearing conditions, comparisons of the distance along the road at which signs were identified were made using repeated measures ANOVA. Paired t-tests were used to compare driving performance for the two conditions.ResultsThere was no statistical difference in sign identification distance between PAL and MFLC for signs 32.0 m from the road side of the road (182 ± 46 m for MFCL; 205 ± 45 m for PAL; P = 0.07) or 51.4 m from the side of the road (204 ± 43 m for MFCL; 216 ± 36 m for PAL; P = 0.3). Only signs 70.2 m from the roadside showed a significant difference (207 ± 42 m with MFCL; 232 ± 39 m with PAL; P = 0.01), All distances were greater than those required to safely stop a vehicle. There were no significant differences in the driving performance metrics between the refractive corrections.ConclusionDriving performance metrics were similar for MFCL and PAL spectacles. Sign identification distances with both eyewear types were well within the distances required for safe vehicle stopping. 相似文献
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目的 介绍国内外智能假肢膝关节研究的主要进展,为相关领域的研发提供可借鉴的思路.方法 基于机械结构、调控方式、驱动方式,对典型假肢膝关节进行了分类比较,并从假肢穿戴者的运动意图识别、驱动控制、人机协调控制等方面做了分析.此外,对假肢膝关节在智能化研究与安全性、个性化与通用性、人机共融技术、科学伦理、关键技术等方面需要注意的问题,提出了研发要点和技术思路.结论 智能假肢膝关节应注重安全性与稳定性,规避用户在使用过程中的潜在危险因素;实现假肢控制参数的自整定和灵活适配,在个性化与通用性上达到平衡;综合考虑人—机—环境因素,实现协调控制;坚持"以人为本",在技术方法和科学伦理两个方面开展医工结合的研究;突破关键技术限制,建立完善的社会医疗康复保障服务体系. 相似文献