A probabilistic risk assessment framework considering lane-changing behavior interaction

Huang, Heye; Wang, Jianqiang; Cong, Fengyu; Zheng, Xunjia; Yang, Yibin; Liu, Jinxin; Wu, Xiangbin; Xu, Qing

doi:10.1007/s11432-019-2983-0

Cited by 43 publications

(16 citation statements)

References 35 publications

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“…Therefore, vehicles will tend to drive along the center line of the road as far as possible to reduce lateral risks, and stay away from the front vehicle as far as possible during the following process to ensure safety. The data results are also consistent with previous studies (26,27).…”

Section: Trajectory Probability Distributionsupporting

confidence: 93%

Probabilistic Situation Assessment for Intelligent Vehicles with Uncertain Trajectory Distribution

Huang

Liu

Zheng

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Situation assessment is crucial for intelligent vehicles, enabling detection of potential risks to dynamic and complex traffic environments. In this paper, we propose a unified framework that tackles the coupling relationships between traffic participants and quantifies the possible range of vehicle trajectory generation and the expected losses caused by risk source attributes in the driving process. We first apply the state space trajectory planning scheme based on a sampling algorithm to generate the path candidates; each feasible path is designed through a parametric cubic spline. Then, to evaluate the risk range in the driving process, we quantify the interaction of traffic participants, and employ the principle of least action to calculate the cost of each feasible path when achieving the destination. The probability distribution map, namely the possible range of driving trajectories, can be obtained based on the path cost. Furthermore, the vehicle-to-vehicle interaction is calculated based on the equivalent force, which estimates the expected accident losses. Finally, the vehicle trajectory prediction and the expected loss are combined to output the probabilistic situation assessment of intelligent vehicles. The algorithm is implemented in different scenarios and applied to the trajectory planning process. Results demonstrate that, compared with the classical situation assessment metric, the developed method can determine and accurately identify the influence range of driving risk in real-time, predict a dangerous situation earlier, and ensure the vehicle avoids obstacles in advance.

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Section: Trajectory Probability Distributionsupporting

confidence: 93%

Probabilistic Situation Assessment for Intelligent Vehicles with Uncertain Trajectory Distribution

Huang

Liu

Zheng

et al. 2021

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

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“…(2) The conversion rules of FTA and BRB are constructed. (3) The reasoning process of the model is explained.…”

Section: Risk Assessment Model Based On Fta and Brbmentioning

confidence: 99%

“…For many high-risk industries, a practical risk assessment may prevent losses in time or it eliminates the damage to the system as much as possible. It is widely used to identify the risk of devices used in nuclear power plants, building, electricity, 1 aviation, internet technology (IT), 2 transport, 3 finance, the environment, and industry. [4][5][6] In the risk assessment of the optimal maintenance of complex industrial systems, various methods have been applied.…”

Section: Introductionmentioning

confidence: 99%

A new risk assessment method based on belief rule base and fault tree analysis

Zhu

Liu

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

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Risk assessment methods are often used in complex industrial systems to avoid risks and reduce losses. The existing methods have not effectively solved the problems of lack of evaluation data and the interpretability of the entire evaluation process. This paper proposes a new risk assessment model based on the belief rule base (BRB) and Fault Tree Analysis (FTA). The FTA algorithm overcomes the difficulties of traditional BRB model in obtaining expert knowledge, clear indicators, and establishing logical relationships. This method establishes FTA rules based on the BRB model and expands the knowledge base through the FTA algorithm. A Bayesian network is applied as a conversion bridge between the FTA and BRB model. In addition, the model is optimized to reduce the uncertainty in the model. The method proposed is described by a case and its effectiveness is verified.

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“…Another problem is the risk assessment of the environment which determines the reward function of the decisionmaking process. 26 The main risk indicators used are the TTC, the TH, the time-to-brake (TTB), the time-tosteer (TTS), etc. [27][28][29][30] Among them, TTC and TH are the most representative indicators because they contain information about the distance and the time.…”

Section: Related Literaturementioning

confidence: 99%

An IMM-based POMDP decision algorithm using collision-risk function in mandatory lane change

Huang

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to make safe and reasonable decisions in some high-risk environments such as the mandatory lane change, we propose an IMM-based partially observable Markov decision process (POMDP) decision algorithm using the collision-risk function which combines the time-to-collision (TTC), the intervehicular time (IT), and the collision function for mandatory lane change. The newly proposed collision-risk function contains two parts: the vehicle impact factor and the collision function, which is used to assess the risk and determines whether the autonomous vehicle collides with surrounding vehicles. The IMM-base POMDP is used for decision-making and we apply the Monte Carlo Tree Search (MCTS) to solve the problem. In the decision-making process, the belief state is obtained by the Interacting Multiple Model (IMM) algorithm. With the collision-risk function and the probability distribution of the states of surrounding vehicles in the future, the proposed POMDP decision algorithm can determine whether the autonomous vehicle accelerates lane changing or decelerates lane changing, and obtain the acceleration corresponding to each path point. Finally, in order to verify the effectiveness of the algorithm, we perform a driver-in-the-loop simulation through Prescan. We use aggressive driver and conservative driver to control the rear vehicle of the target lane, respectively. Simulation results show that the proposed algorithm can accurately predict the accelerations of surrounding vehicles and make safe and reasonable decisions under two scenarios, which is superior to the general POMDP.

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A probabilistic risk assessment framework considering lane-changing behavior interaction

Cited by 43 publications

References 35 publications

Probabilistic Situation Assessment for Intelligent Vehicles with Uncertain Trajectory Distribution

Probabilistic Situation Assessment for Intelligent Vehicles with Uncertain Trajectory Distribution

A new risk assessment method based on belief rule base and fault tree analysis

An IMM-based POMDP decision algorithm using collision-risk function in mandatory lane change

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