Cyclist detection and tracking based on multi-layer laser scanner

Zhang, Mingfang; Fu, Rui; Guo, Yuanhao; Wang, Li; Wang, Pangwei; Deng, Hui

doi:10.1186/s13673-020-00225-x

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…Furthermore, the datasets that AI use to train themselves have images that have concentrated on cars and the lack of bicycle images (ibid). This is corroborated by several studies that identify cyclist detection as a weak point (Ahmed et al 2019a , b ; Botello et al 2019 ; Masalov et al 2019 ; Pyrialakou et al 2020 ; Zhang et al 2020 ). AV safety systems need to earn the trust of society as well as understand all the ticks and quirks of erratic behaviour such as track stands, sudden manoeuvres (i.e.…”

Section: Defining Ai Systems From a Bicycle-user Perspectivesupporting

confidence: 59%

“…Further to Hagenzieker et al the gap in research intersecting the topics of AVs and cyclists was a demonstrable omission that needed to be resolved to better understand the implications of AVs on cycling (Ahmed et al 2019a , b ; Botello et al 2019 ; Coelho and Guarnaccia 2020 ; Eldesokey et al 2017 ; Kress et al 2018 ; Penmetsa et al 2019 ; Wang and Akar 2019 ; Zhang et al 2020 ). Of note is the fact that, while there is research on AVs, the primary research focus is either on hypothetical future users or is too thematically broad to draw adequate conclusions for cyclists.…”

Section: What Can Be Done To Avoid Further Oppression In An Age Of Au...mentioning

confidence: 99%

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Cyclists and autonomous vehicles at odds

Gaio

Cugurullo

2022

AI & Soc

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Consequential historical decisions that shaped transportation systems and their influence on society have many valuable lessons. The decisions we learn from and choose to make going forward will play a key role in shaping the mobility landscape of the future. This is especially pertinent as artificial intelligence (AI) becomes more prevalent in the form of autonomous vehicles (AVs). Throughout urban history, there have been cyclical transport oppressions of previous-generation transportation methods to make way for novel transport methods. These cyclical oppressions can be identified in the baroque and modernist periods, and a third oppression may occur in the contemporary period. To explore the idea of a third oppression, we focus on the bicycle and outline the history of cycling to understand how historical mode oppression unfolded. We then present several social and political factors that contributed to the oppression of cycling and share recommendations for how to avoid future oppressions including political, social, and design actions for researchers and policymakers to take. This paper argues that priorities for AI-enabled mobility and cyclist needs be advanced in proportion to the extent that they contribute to societal goals of urban containment, public realm, and proximal cities. Additionally, future mobility evolutions should prioritise mobility justice and mode choice over inducing a singular transportation method.

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Section: Defining Ai Systems From a Bicycle-user Perspectivesupporting

confidence: 59%

Section: What Can Be Done To Avoid Further Oppression In An Age Of Au...mentioning

confidence: 99%

Cyclists and autonomous vehicles at odds

Gaio

Cugurullo

2022

AI & Soc

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“…In the experiment, the partition-based DSC method (SDSC) [27] is used for comparison, because it not only considers the spatial distribution of the point cloud but also still applies the traditional DSC within each sub-region. To ensure the reliability of the experiment, the relevant parameters are first designed.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The detected area is evenly divided into several regions beforehand. Then, the optimal parameters of each region are calculated by the geometric structure of boundary points [26], or the original partition is further optimized by considering the change rate of point density in different regions [27]. Finally, DSCs with different parameters are adopted for each region to realize object classification.…”

Section: (B) Space Partitionmentioning

confidence: 99%

A New Density-Based Clustering Method Considering Spatial Distribution of Lidar Point Cloud for Object Detection of Autonomous Driving

Gao

Han

et al. 2021

Electronics

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Lidar is a key sensor of autonomous driving systems, but the spatial distribution of its point cloud is uneven because of its scanning mechanism, which greatly degrades the clustering performance of the traditional density-based spatial clustering of application with noise (DSC). Considering the outline feature of detected objects for intelligent vehicles, a DSC-based adaptive clustering method (DAC) is proposed with the adoption of an elliptic neighborhood, which is designed according to the distribution properties of the point cloud. The parameters of the ellipse are adaptively adjusted with the location of the sample point to deal with the uniformity of points in different ranges. Furthermore, the dependence among different parameters of DAC is analyzed, and the parameters are numerically optimized with the KITTI dataset by considering comprehensive performance. To verify the effectiveness, a comparative experiment was conducted with a vehicle equipped with three IBEO LUX8 lidars on campus, and the results show that compared with DSC using a circular neighborhood, DAC has a better clustering performance and can notably reduce the rate of over-segmentation and under-segmentation.

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“…The processing of input data starts with input layer and passes through hidden layers and then to output layer. The construction of FFNN can be done from different units like binary McCulloch–Pitts neurons [ 22 , 23 ]. An example of an FFNN is a perceptron.…”

Section: Introductionmentioning

confidence: 99%

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Rouf

Malik

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic.

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Cyclist detection and tracking based on multi-layer laser scanner

Cited by 11 publications

References 24 publications

Cyclists and autonomous vehicles at odds

Cyclists and autonomous vehicles at odds

A New Density-Based Clustering Method Considering Spatial Distribution of Lidar Point Cloud for Object Detection of Autonomous Driving

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

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