An Interacting Multiple Model for Trajectory Prediction of Intelligent Vehicles in Typical Road Traffic Scenario

Gao, Hongbo; Qin, Yechen; Hu, Chuan; Liu, Yuchao; Li, Keqiang

doi:10.1109/tnnls.2021.3136866

Cited by 48 publications

(20 citation statements)

References 44 publications

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“…Obstacle negotiation is the premise for robots to overcome obstacles (Zhu et al, 2021), and the methods for mobile robots to achieve autonomous obstacle negotiation can be divided into two categories: environment-based motion planning obstacle negotiation method (Li et al, 2016) and information-based online control obstacle negotiation method (Vincent and Sun, 2012;Sokolov et al, 2017). Either method is inseparable from the perception of the surroundings (Xie et al, 2018;Gao et al, 2021aGao et al, , 2021b, and the environment-based motion planning obstacle negotiation method has higher requirements for obstacle recognition (Gao et al, 2022(Gao et al, , 2021a(Gao et al, , 2021b. The result of obstacle detection largely determines the success or failure of autonomous obstacle negotiation.…”

Section: Introductionmentioning

confidence: 99%

Towards an obstacle detection system for robot obstacle negotiation

Wang,

Zhang,

Fan

et al. 2024

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Purpose To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles. Design/methodology/approach The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method. Findings The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage. Originality/value This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

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Section: Introductionmentioning

confidence: 99%

Towards an obstacle detection system for robot obstacle negotiation

Wang,

Zhang,

Fan

et al. 2024

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“…In recent years, with the rapid development of artificial intelligence technology, the learning-based algorithms attract increasing attention in autonomous navigation and decisionmaking systems (Huang et al, 2020;Ferreira et al, 2022;Rødseth et al, 2022). According to different principles, such systems are divided into decision-making methods related to deep learning (Chen et al, 2020;Grigorescu et al, 2020) and the interactive learning theory or reward mechanism (Gao et al, 2018;Gao et al, 2021). Within the context of MASS, some scholars used deep learning for autonomous ship collision avoidance parameter training (Chen et al, 2021), ship behavior prediction (Murray and Perera, 2021) and trajectory prediction (Liu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation

et al. 2023

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Reinforcement learning (RL) has shown superior performance in solving sequential decision problems. In recent years, RL is gradually being used to solve unmanned driving collision avoidance decision-making problems in complex scenarios. However, ships encounter many scenarios, and the differences in scenarios will seriously hinder the application of RL in collision avoidance at sea. Moreover, the iterative speed of trial-and-error learning for RL in multi-ship encounter scenarios is slow. To solve this problem, this study develops a novel intelligent collision avoidance algorithm based on approximate representation reinforcement learning (AR-RL) to realize the collision avoidance of maritime autonomous surface ships (MASS) in a continuous state space environment involving interactive learning capability like a crew in navigation situation. The new algorithm uses an approximate representation model to deal with the optimization of collision avoidance strategies in a dynamic target encounter situation. The model is combined with prior knowledge and International Regulations for Preventing Collisions at Sea (COLREGs) for optimal performance. This is followed by a design of an online solution to a value function approximation model based on gradient descent. This approach can solve the problem of large-scale collision avoidance policy learning in static-dynamic obstacles mixed environment. Finally, algorithm tests were constructed though two scenarios (i.e., the coastal static obstacle environment and the static-dynamic obstacles mixed environment) using Tianjin Port as an example and compared with multiple groups of algorithms. The results show that the algorithm can improve the large-scale learning efficiency of continuous state space of dynamic obstacle environment by approximate representation. At the same time, the MASS can efficiently and safely avoid obstacles enroute to reaching its target destination. It therefore makes significant contributions to ensuring safety at sea in a mixed traffic involving both manned and MASS in near future.

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“…The driver’s physical state requires careful scrutiny through the continuous monitoring of behavior and body signals in order to bring down the fatality rate and prevent road accidents, ensuring driver safety and well-being [ 4 ]. An alternative control method for the lateral motion problem, based on a bounded equivalent function, the vehicle kinematic model, and the Taylor series expansion, has been developed, which ensures both robustness and control accuracy [ 5 ]. The author ensured the consistent segmentation of sequential points within behavior actions and provides better accuracy [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning

Murugan

Sivakumar

Kavitha

et al. 2023

Sensors

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Driving safely is crucial to avoid death, injuries, or financial losses that can be sustained in an accident. Thus, a driver’s physical state should be monitored to prevent accidents, rather than vehicle-based or behavioral measurements, and provide reliable information in this regard. Electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), and surface electromyography (sEMG) signals are used to monitor a driver’s physical state during a drive. The purpose of this study was to detect driver hypovigilance (drowsiness, fatigue, as well as visual and cognitive inattention) using signals collected from 10 drivers while they were driving. EOG signals from the driver were preprocessed to remove noise, and 17 features were extracted. ANOVA (analysis of variance) was used to select statistically significant features that were then loaded into a machine learning algorithm. We then reduced the features by using principal component analysis (PCA) and trained three classifiers: support vector machine (SVM), k-nearest neighbor (KNN), and ensemble. A maximum accuracy of 98.7% was obtained for the classification of normal and cognitive classes under the category of two-class detection. Upon considering hypovigilance states as five-class, a maximum accuracy of 90.9% was achieved. In this case, the number of detection classes increased, resulting in a reduction in the accuracy of detecting more driver states. However, with the possibility of incorrect identification and the presence of issues, the ensemble classifier’s performance produced an enhanced accuracy when compared to others.

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An Interacting Multiple Model for Trajectory Prediction of Intelligent Vehicles in Typical Road Traffic Scenario

Cited by 48 publications

References 44 publications

Towards an obstacle detection system for robot obstacle negotiation

Towards an obstacle detection system for robot obstacle negotiation

Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation

An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning

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