A new active safety distance model of autonomous vehicle based on sensor occluded scenes

Yuan, Chaochun; Wang, Tong; Shen, Jie; He, Youguo; Weng, Shuofeng

doi:10.1080/02286203.2019.1706068

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Some works present a novel simulation-based within the perception layer to obtain the performance of sensors [18]. Yuan [19] introduces an innovative perception method with deep learning technologies. In many cases, sensors consist of thousands of raw data from sensors to check their diversity manually.…”

Section: Sensor Simulation Analysismentioning

confidence: 99%

Simulation based perception testing for autonomous vehicles

Yang¹

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Perception Testing technologies are widely applied in various scenarios, like industrial and academic research applications for autonomous driving systems. Accurate and robust autonomous driving simulation perception is pivotal for safety-guidance autonomous vehicles (AV). The autonomous vehicle system is facing the main challenges of a complex real-world environment with multi sensors' performance and their neighborhood view with an uncertain environment. The perception module exploits deep learning models to detect surrounding obstacles, including their types, positions, and velocities. However, the issue of LiDAR performance, which is the prediction of multi obstacle properties based on their deep learning model, remains unresolved. In addition, autonomous Vehicle systems rely on deep learning models to collect and modify raw point cloud data. It's crucial to check autonomous vehicles' robustness in several scenarios to generate automotive vehicles more safety and reliable. At this stage, it is easier to perform sensor integration to simulate sensors' performance for Autonomous Driving at various levels of verification. This thesis utilizes a realistic LiDAR point cloud and compares the difference between real-world and simulation environments to test perception modules for autonomous driving platform systems. This thesis proposes a simulation-based testing platform for autonomous vehicles to discover the potential shortage of perception by analysing huge scenarios and testing the suitable sensor performance in multi particular levels of the automated driving platforms. Additionally, this thesis introduces a simulation-based testing method, which involves multi-sensor configurations and includes virtual environment testing with various scenarios. To deal with real-time traffic scenarios, an effective way is utilized to search for the closest scenarios. Also, the proposed method has been tested in popular autonomous vehicle platforms and simulators . To utilize the existing methodology, we perform industry platforms on Baidu Apollo and guide it to assess the quality and enhance the perception system based on a simulation-based testing platform.

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Section: Sensor Simulation Analysismentioning

confidence: 99%

Simulation based perception testing for autonomous vehicles

Yang¹

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Machine Learning-Based Forward Collision Avoidance System: A Case Study for the Kayoola EVS

Ziryawulawo,

Mbarebaki,

Anael

2024

Progress in IS

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Identification of pedestrian motion feature in mixed traffic conditions and anti-collision algorithm of autonomous vehicle

Yuan,

Wu,

Shen

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve the safety of pedestrians crossing the road in the mixed traffic conditions, this study proposed an active collision avoidance method based on the prediction of pedestrian trajectory. A convolutional neural network is applied to identify the motion feature of pedestrians crossing the road in the image of an automated driving environment with vehicular sensors. Then combined with the pedestrian motion parameters, a new Kalman filtering algorithm is proposed to analyze the change of pedestrian motion feature and predict the trajectory of the pedestrian. Furthermore, a PDS (Pedestrian, Distance, and Speed) estimated braking distance model based on pedestrian characteristics, the distance between pedestrian and vehicle, and the speed of the vehicle is established in this study for the autonomous vehicle controlling speed in advance to avoid risks. It improves both the crossing road pedestrian safety and efficiency of traffic. Eventually, simulations based on CarSim/Simulink are designed to verify the validity of the method. Results show that the method proposed can effectively predict pedestrian trajectories and realize active collision avoidance under the time delay of the detection link.

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A new active safety distance model of autonomous vehicle based on sensor occluded scenes

Cited by 4 publications

References 18 publications

Simulation based perception testing for autonomous vehicles

Simulation based perception testing for autonomous vehicles

Machine Learning-Based Forward Collision Avoidance System: A Case Study for the Kayoola EVS

Identification of pedestrian motion feature in mixed traffic conditions and anti-collision algorithm of autonomous vehicle

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