A co-evolutionary lane-changing trajectory planning method for automated vehicles based on the instantaneous risk identification

Wu, Jiabin; Chen, Xiaohua; Bie, Yiming; Zhou, Wei

doi:10.1016/j.aap.2022.106907

Cited by 19 publications

(12 citation statements)

References 32 publications

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“…The accurate assessment of potential conflicts or crashes and the identification of key indicators of injury risk enable the development of proactive safety measures and targeted interventions. Transportation agencies and policymakers can leverage the insights gained from this study to prioritize infrastructure improvements, identify hazardous driving behaviors and blackpoint roadway locations, and allocate resources effectively for traffic enforcement [59,60]. Additionally, automotive manufacturers can utilize the identified determinants of injury risk to inform the design of vehicle structures and incorporate advanced safety technologies that address specific crash scenarios and impact positions [61,62].…”

Section: Discussionmentioning

confidence: 99%

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

et al. 2023

Applied Sciences

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Pre-crash injury risk (IR) assessment is essential for guiding efforts toward active vehicle safety. This work aims to conduct crash severity assessment using pre-crash information and establish the intrinsic mechanism of IR with proper interpretation methods. The impulse–momentum theory is used to propose novel a priori formulations of several severity indicators, including velocity change (ΔV), energy equivalent speed (EES), crash momentum index (CMI), and crash severity index (CSI). Six IR models based on different machine learning methods were applied to a fusion dataset containing 24,082 vehicle-level samples. Prediction results indicate that the pre-crash indicators (PCIs) are more influential than the commonly used basic crash information because the average accuracy of six models can be improved by 14.35% after utilizing PCIs. Furthermore, the features’ importance and their marginal effects are interpreted based on parameter estimation, Shapley additive explanation value, and partial dependence. The ΔV, EES, and CMI are identified as the determinant indicators of the potential IR, and their partial distributions are significantly influenced by the crash type and impact position. Based on partial dependence probabilities, the study establishes decision thresholds for PCIs for each severity category for different impact positions, which can serve as a useful reference for developing targeted safety strategies. These results suggest that the proposed method can effectively improve pre-crash IR assessment, which can be readily transferred to safety-related modeling in an active traffic management system.

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Section: Discussionmentioning

confidence: 99%

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

et al. 2023

Applied Sciences

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“…Additionally, these methods are often used in conjunction with other planning approaches for trajectory smoothing. Wu et al [ 18 ] introduced a collaborative evolution lane-changing trajectory planning method, achieving intelligent vehicle lane changes by integrating curve interpolation methods with deep learning models.…”

Section: Related Workmentioning

confidence: 99%

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

Liu,

Diao

2024

PLoS ONE

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As autonomous driving technology continues to advance and gradually become a reality, ensuring the safety of autonomous driving in complex traffic scenarios has become a key focus and challenge in current research. Model-free deep reinforcement learning (Deep Reinforcement Learning) methods have been widely used for addressing motion planning problems in complex traffic scenarios, as they can implicitly learn interactions between vehicles. However, current planning methods based on deep reinforcement learning exhibit limited robustness and generalization performance. They struggle to adapt to traffic conditions beyond the training scenarios and face difficulties in handling uncertainties arising from unexpected situations. Therefore, this paper addresses the challenges presented by complex traffic scenarios, such as signal-free intersections. It does so by first utilizing the historical trajectories of adjacent vehicles observed in these scenarios. Through a Variational Auto-Encoder (VAE) based on the Gated Recurrent Unit (GRU) recurrent neural network, it extracts driver style features. These driver style features are then integrated with other state parameters and used to train a motion planning strategy within an extended reinforcement learning framework. This approach ultimately yields a more robust and interpretable mid-to-mid motion planning method. Experimental results confirm that the proposed method achieves low collision rates, high efficiency, and successful task completion in complex traffic scenarios.

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“…Traffic conflict discrimination, especially automatic traffic conflict discrimination, has developed at a high rate along with the rise of autonomous driving. Considering the safe interaction between the subject vehicle and the surrounding vehicles in the study of a co-evolutionary lane-changing trajectory planning method for automated vehicles, Wu et al established a mathematical model for the temporal and spatial risk identification of a lane change event based on the fault tree analysis method [24]. Xie et al introduced Time to Collision (TTC) to identify rear-end conflict risk for adjacent vehicles, and proposed Hidden Markov models (HMMs) to model the rear-end conflicts at five-minute intervals.…”

Section: Discrimination Of Traffic Conflictsmentioning

confidence: 99%

Sequence Calculation and Automatic Discrimination of Vehicle Merging Conflicts in Freeway Merging Areas

Wang

et al. 2022

Sustainability

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The freeway is a continuous flow facility that improves the accessibility and operational efficiency of the road network. However; freeway merging areas are accident-prone areas. In order to investigate the reasons for the high occurrence of accidents in merging areas, this paper considers the dynamic nature of traffic conflicts, constructs a sequence model of merging conflicts with Time Difference to Collision (TDTC) as the index, and implements automatic identification of merging conflicts based on the LightGBM algorithm. A UAV was used to collect vehicle trajectory data at the Guanghe Freeway in Guangzhou to verify the accuracy of automatic identification, with an accuracy rate of 91%. The results show that the most important feature of severe conflicts is the choice of the merging position. In addition, the most important feature of general conflicts is the standard deviation of speed before merging. Lastly, the most important feature of minor conflicts is the longitudinal speed difference between the ramp and mainline vehicles.

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A co-evolutionary lane-changing trajectory planning method for automated vehicles based on the instantaneous risk identification

Cited by 19 publications

References 32 publications

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

Sequence Calculation and Automatic Discrimination of Vehicle Merging Conflicts in Freeway Merging Areas

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