Personalized Lane-Change Assistance System With Driver Behavior Identification

Zhu, Bing; Yan, Shude; Zhao, Jian; Deng, Weiwen

doi:10.1109/tvt.2018.2867541

Cited by 76 publications

(30 citation statements)

References 23 publications

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“…The HIL has recently become a tool to ensure vehicle automation. Driver‐in‐the‐loop simulation has been realised to proceed the training phase for an intelligent/autonomous navigation approach while investigating safety‐related issues [119]. As it can be seen from the literature, the HIL opens vast possibilities as: easy development/validation of control and diagnosis functions calibrating and exploring results of different configurations revealing real‐time data analysis and run‐time properties optimising automotive functions by results issued from various performance‐cost tests (in terms of memory, energy consumption etc.…”

Section: Hardware In the Loopmentioning

confidence: 99%

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Lakhal

Nasri

Adouane

et al. 2020

IET intell. transp. syst

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The in‐vehicular networked control system is among the most critical embedded processes. The controller area network (CAN) has prevailed intra‐vehicle communication for decades. Meanwhile, requirements of future transportation systems are expected to emphasise the in‐vehicle communication complexity, which endangers the reliability/safety of the intelligent navigation. At first, this study reviews the recent solutions proposed to overcome the CAN expanding complexity. Challenges that tomorrow's intelligent vehicles may raise for CAN reliability are investigated. The comprehensive coverage of current research efforts to remove the impact of these challenges is presented. Further, the in‐vehicle system reliability of future automated vehicles is also related to the fault diagnosis performances. Hence, different classes of system‐level diagnosis strategies are compared relatively to the requirements of automotive embedded networks. Furthermore, to thoroughly cover CAN reliability engineering issues, focus is given to the automotive validation techniques. The hardware in the loop, real‐time analysis and computer‐aided‐design tools intervene in various phases along the in‐vehicular network life cycle. Parameters that stand behind the efficiency and accuracy of these techniques in validating the new generation of vehicles are analysed. The authors finally draw some deductive predictions about the future directions related to the reliability of the intelligent transportation system in‐vehicular communication.

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Section: Hardware In the Loopmentioning

confidence: 99%

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Lakhal

Nasri

Adouane

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

show abstract

“…Multivariate statistical forecasting method has been widely used in the industry (Yin et al, 2016). Recently, several studies have applied such method towards personalized driving (Schnelle et al, 2017; Zhao et al, 2018; Zhu et al, 2018). To further validate the prediction capacity, we compare the GMM with the principal component regression (PCR), which is a widely known multivariate statistical forecasting method.…”

Section: Verification and Analysismentioning

confidence: 99%

Longitudinal and lateral dynamics control of automatic lane change system

Wang

Zheng

Zong

2019

Transactions of the Institute of Measurement and Control

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The urgency of extending functionalities of current advanced driver assistance systems (ADAS), and eventually progressing to highly automated highway driving necessitates the design of automatic lane change system. This paper presents an automatic lane change system targeting on discretional lane change scenario in highway driving. Assuming motion signals of all participants to be available, the MOBIL model is employed as the decision module. The functionality of the adaptive cruise controller is extended to realize dual-target tracking, so as to prevent abrupt acceleration change provoked by the sudden switch of the leading vehicle during the lane change. For the lateral part, a hierarchical trajectory planning algorithm, which combines parametric function and learning-based technique, is proposed to account for uncertainties of driver characteristics in different traffic situations. The trajectory is then tracked by a low-level controller based on model predictive control (MPC) theory, which employs a force input model to predict the motion of the vehicle, and formulates environment envelope and handling limits as constraints. The proposed algorithm is validated through simulations of typical scenarios. Overall, this paper lays a solid foundation for the prototype of ADAS regarding lane change.

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“…This property can be exploited to eliminate and filter out the line segments that do not constitute lanes [ 29 , 30 , 31 ]. A number of approaches have been proposed in the literature for tracking a single lane, such as [ 14 , 17 , 31 , 32 , 33 , 34 , 35 ]. In [ 17 ], color, gradient, and line clustering information are used to improve the extraction of lane markings.…”

Section: Introductionmentioning

confidence: 99%

“…Our approach also uses splines, but our tracking approach is significantly different to the one in [ 32 ]. In [ 33 , 34 ], techniques for personalized lane-change maneuvering are discussed. They use driver-specific behaviors, collected as part of the system, to improve the results.…”

Section: Introductionmentioning

confidence: 99%

A Multilane Tracking Algorithm Using IPDA with Intensity Feature

Akbari

Thiyagalingam

Lee

et al. 2021

Sensors

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Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as 5%, 12%, and 3% improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers 57%, 31%, 4%, and 9× improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained.

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Personalized Lane-Change Assistance System With Driver Behavior Identification

Cited by 76 publications

References 23 publications

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Longitudinal and lateral dynamics control of automatic lane change system

A Multilane Tracking Algorithm Using IPDA with Intensity Feature

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