Lane Detection and Trajectory Tracking Control of Autonomous Vehicle Based on Model Predictive Control

Hu, Jianjun; Xiong, Songsong; Zha, Junlin; Fu, Chunyun

doi:10.1007/s12239-020-0027-6

Cited by 46 publications

(20 citation statements)

References 18 publications

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“…Lee et al [ 8 ] proposed an efficient and robust lane detection and tracking algorithm that uses the region of interest (ROI) of an input image to reduce redundant image data; the algorithm is divided into three steps: initialization, lane detection, and lane tracking. Hu et al [ 9 ] proposed a new method of lane detection combined with model predictive control for effective lane information extraction and trajectory tracking by using a dynamic ROI extraction method based on longitudinal vehicle speed changes to improve the real-time performances and adaptability of traditional image information extraction methods. In a recent study, lane lines were detected with perspective transformation, threshold processing, mask operations and sliding window optimization [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

Real-time lane detection model based on non bottleneck skip residual connections and attention pyramids

et al. 2021

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The security of car driving is of interest due to the growing number of motor vehicles and frequent occurrence of road traffic accidents, and the combination of advanced driving assistance system (ADAS) and vehicle-road cooperation can prevent more than 90% of traffic accidents. Lane detection, as a vital part of ADAS, has poor real-time performance and accuracy in multiple scenarios, such as road damage, light changes, and traffic jams. Moreover, the sparse pixels of lane lines on the road pose a tremendous challenge to the task of lane line detection. In this study, we propose a model that fuses non bottleneck skip residual connections and an improved attention pyramid (IAP) to effectively obtain contextual information about real-time scenes and improve the robustness and real-time performance of current lane detection models. The proposed model modifies the efficient residual factorized pyramid scene parsing network (ERF-PSPNet) and utilizes skip residual connections in non bottleneck-1D modules. A decoder with an IAP provides high-level feature maps with pixel-level attention. We add an auxiliary segmenter and a lane predictor side-by-side after the encoder, the former for lane prediction and the latter to assist with semantic segmentation for classification purposes, as well as to solve the gradient disappearance problem. On the CULane dataset, the F1 metric reaches 92.20% in the normal scenario, and the F1 metric of the model is higher than the F1 metrics of other existing models, such as ERFNet-HESA, ENet_LGAD, and DSB+LDCDI, in normal, crowded, night, dazzling light and no line scenarios; in addition, the mean F1 of the nine scenarios reached 74.10%, the runtime (time taken to test 100 images) of the model was 5.88 ms, and the number of parameters was 2.31M, which means that the model achieves a good trade-off between real-time performance and accuracy compared to the current best results (i.e., a running time of 13.4 ms and 0.98M parameters).

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Section: Related Workmentioning

confidence: 99%

Real-time lane detection model based on non bottleneck skip residual connections and attention pyramids

et al. 2021

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“…The investigated method does not implement a sensor fusion technique between the stereocamera and LiDAR, since it is intended to build a local map from the sensors, even in the case of a failure on one of the two sensors. This task is crucial to enable any further trajectory planning and control algorithm for autonomous driving [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments

Feraco¹,

Bonfitto²,

Amati³

et al. 2022

Komunikácie

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This paper presents a redundant multi-object detection method for autonomous driving, exploiting a combination of Light Detection and Ranging (LiDAR) and stereocamera sensors to detect different obstacles. These sensors are used for distinct perception pipelines considering a custom hardware/software architecture deployed on a self-driving electric racing vehicle. Consequently, the creation of a local map with respect to the vehicle position enables development of further local trajectory planning algorithms. The LiDAR-based algorithm exploits segmentation of point clouds for the ground filtering and obstacle detection. The stereocamerabased perception pipeline is based on a Single Shot Detector using a deep learning neural network. The presented algorithm is experimentally validated on the instrumented vehicle during different driving maneuvers.

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“…Then, an improved RANdom SAmple Consensus algorithm has been introduced using the feedback from lane edge angles and the curvature of lane history to prevent false LD. Dynamic ROI extraction, edge detection, and Hough straight-line detection have been applied to extract the lane line in [3]. The model predictive control has been applied to track the extracted lane line and the front wheel steering angle has been corrected by the fuzzy controller based on the yaw angle and the yaw rate.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…The VP is considered to be the point in which the most extracted lines of image are intersected. Adaptive ROI based on the longitudinal velocity changes of the vehicle is introduced [3] in which the upper boundary line moves down and up according to autonomous vehicle speed. The ROI extraction based on the minimum safe distance between the ego vehicle and the vehicle in front of it is proposed in [23].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

Ghanem

Kanungo

Panda

et al. 2021

Complex Intell. Syst.

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Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements.

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Lane Detection and Trajectory Tracking Control of Autonomous Vehicle Based on Model Predictive Control

Cited by 46 publications

References 18 publications

Real-time lane detection model based on non bottleneck skip residual connections and attention pyramids

Real-time lane detection model based on non bottleneck skip residual connections and attention pyramids

Redundant Multi-Object Detection for Autonomous Vehicles in Structured Environments

Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure

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