Perception of Vehicle and Traffic Dynamics Using Visual-Inertial Sensors for Assistive Driving

Zhang, Yinlong; Liang, Wei; He, Hongsheng; Tan, Jindong

doi:10.1109/robio.2018.8665053

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…In this study, the Random Sample Consensus (RANSAC) algorithm (Figure 2) was employed for lane detection based on monocular red-green-blue (RGB) images, and could eliminate serious errors and interference of error points in the data (14,(28)(29)(30)(31). Compared with other lane detection algorithms, such as ResNet and Hough transform, the RANSAC model is found to have better accuracy (Table 2).…”

Section: Lane Detection Algorithmsmentioning

confidence: 99%

Surrogate Evaluation Model for Assessing Lane Detection Reliability of Automated Vehicles in Complex Road Environments

Chai

Liu

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The reliability of environmental perception is the key to ensuring the stability and safety of autonomous vehicles. Complex road environments pose attentional challenges to automated driving, especially for vehicle perception systems. Therefore, evaluating the performance of autonomous driving algorithms in complex road environments is of great significance. However, as road geometry is complex, with many factors, including curvature, slope, lane marking conditions, and so on, the existing approach in assessing an algorithm through detection tests is of low efficiency. Therefore, this study relies on failure prediction modeled by extreme gradient boosting and recursive feature elimination. An evaluation model that could accomplish a quick set of road evaluations is developed. The proposed model generates recognition reliability merely by such features as road design parameters. In addition, road curvature is the most significant environmental factor affecting the perception of autonomous vehicles. The failure rates of the algorithm in different scenarios demonstrate a negative correlation between the radius of curvature and failure perception. The partial dependence involving the radius of curvature and the perception ability is obtained. Furthermore, other features, such as missing lane lines and illumination conditions, are taken into consideration to explore the potential failure perception mode. The proposed method has a particular significance for finding the failure mode of an autonomous driving perception algorithm.

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Section: Lane Detection Algorithmsmentioning

confidence: 99%

Surrogate Evaluation Model for Assessing Lane Detection Reliability of Automated Vehicles in Complex Road Environments

Chai

Liu

et al. 2023

Transportation Research Record: Journal of the Transportation R

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show abstract

“…In most cases, data confirm the normal distribution, X ∼ N(μ, σ). According to Zhang [37], data that fall outside the [μ − kσ, μ + kσ] range are considered outliers (where k ≥ 3). k � 4 (when 0.01% of the observations are set as outliers) is applied in this study as threshold.…”

Section: Vehicle-control Parametersmentioning

confidence: 99%

Investigating Contributing Factors on Aggressive Driving Based on a Structural Equation Model

Wang

et al. 2022

Journal of Advanced Transportation

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Tailored countermeasures that may significantly improve road traffic safety can be proposed and implemented if the relationship between various associated factors and aggressive driving is well understood. However, this relationship remains unknown, as driving behavior is complex, and the interrelationships among variables are not easy to identify. Considering this situation, this paper constructed a model based on a structural equation model (SEM) and factor analysis (FA), which is a multivariate statistical analysis technique used to analyze structural relationships. The model is applied in a case study using data from the Shanghai Naturalistic Driving Study. In the case study, 16 variables were grouped into five latent factors in the SEM, and the model fits the data well. Compared with other variables, the results show that age had the most significant positive impact on aggressive driving behavior (older drivers exhibited high aggressive driving frequency). Adverse weather negatively impacted driver behavior (lower speed and high longitude acceleration), which in turn negatively affected aggressive driving behavior. In addition, the results show that driver factors (such as age and sex) were the main factors influencing vehicle use (such as hard acceleration), and the environment was the main factor determining risky scenarios, where safety-critical situations increase. This paper provides a reference for defining and determining aggressive driving and a model for exploring the relationship between driving safety factors and aggressive driving, which can be used in real-world applications for improving driving safety with applications in advanced driver-assistance (ADAS) and traffic enforcement safety control systems.

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“…Simultaneously, a broad-spectrum is available for visual sensors and can detect invisible rays to the eye. Such property significantly expands human vision space [7][8][9][10]. However, in everyday natural circumstances, obstacles such as vehicle obstruction, inadequate light, various road twists and curves, and diverse surroundings on both sides have made reliable lane detection challenging [11,12].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle

Waykole¹,

Shiwakoti²,

Stasinopoulos³

2022

Sustainability

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Disruptive technology, especially autonomous vehicles, is predicted to provide higher safety and reduce road traffic emissions. Lane detection and tracking are critical building blocks for developing autonomous or intelligent vehicles. This study presents a lane detecting algorithm for autonomous vehicles on different road pavements (structured and unstructured roads) to overcome challenges such as the low detection accuracy of lane detection and tracking. First, datasets for performance evaluation were created using an interpolation method. Second, a learning-based approach was used to create an algorithm using the steering angle, yaw angle, and sideslip angle as inputs for the adaptive controller. Finally, simulation tests for the lane recognition method were carried out by utilising a road driving video in Melbourne, Australia, and the BDD100K dataset created by the Berkeley DeepDrive Industrial Consortium. The mean detection accuracy ranges from 97% to 99%, and the detection time ranges from 20 to 22 ms under various road conditions with our proposed algorithm. This lane detection algorithm outperformed conventional techniques in terms of accuracy and processing time, as well as efficiency in lane detection and overcoming road interferences. The proposed algorithm will contribute to advancing the lane detection and tracking of intelligent-vehicle driving assistance and help further improve intelligent vehicle driving safety.

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Perception of Vehicle and Traffic Dynamics Using Visual-Inertial Sensors for Assistive Driving

Cited by 7 publications

References 12 publications

Surrogate Evaluation Model for Assessing Lane Detection Reliability of Automated Vehicles in Complex Road Environments

Surrogate Evaluation Model for Assessing Lane Detection Reliability of Automated Vehicles in Complex Road Environments

Investigating Contributing Factors on Aggressive Driving Based on a Structural Equation Model

Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle

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