Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Wu, Ping; Gao, Feng; Li, Keqiang

doi:10.1109/access.2022.3141075

Cited by 23 publications

(11 citation statements)

References 57 publications

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“…Note that we intentionally positioned the obstacle vehicle slightly ahead of the ego vehicle to simulate an urgent merging situation, which provides better observation of the paths of both vehicles. In addition, we denoted the following path planners for comparative study: i) PF-based planner with longitudinally constant speed (denoted as PF-CS) [16]; ii) PF-based planner with speed planning (denoted as PF-SP) [17]; ii) Proposed PF-based planner with interactive speed optimization (denoted as PF-ISO).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Still, The focus of the study was limited to low-speed obstacles with a single predefined motion. Similarly, Wu et al [17] proposed a PF-based lane-change algorithm for generating human-like trajectories while considering risks, drivers' focus shifts, and speed requirements. However, they did not consider the merging scenario and predefined the obstacle behaviors for observing the ego vehicle's path.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Lin

Tsukada

2022

2022 13th Asian Control Conference (ASCC)

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Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-tovehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Lin

Tsukada

2022

2022 13th Asian Control Conference (ASCC)

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“…Wu et al [ 23 ] took into account the influence of increasingly complex urban environmental information on drivers' lane-changing decisions. Based on the data generated by driving simulators, they extracted two decision rules in the intention generation and implementation stages through rough set theory, providing a theoretical basis for lane-changing decisions in complex driving environments.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction

Feng

Yan

2022

Computational Intelligence and Neuroscience

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With the development of technology, vehicle trajectory prediction and safety decision technology has become an important part of active safety technology. Among them, the vehicle trajectory prediction technology can predict the vehicle position, speed, and other motion states in the predicted period according to the current and historical vehicle running state, and the prediction results can provide support for judging the vehicle safety in the predicted period. In order to analyze the above problems, this study fully extracted the main feature information from the vehicle lane change track data with the help of the powerful nonlinear learning and high pattern recognition ability of support vector machine, and conducted identification modeling for the actual lane change process of the vehicle and predictive analysis of the vehicle lateral movement track. First, the lane-changing behavior of vehicles was analyzed, and the vehicle lane-changing execution stage and 10 influencing factors that could characterize lane-changing behavior were determined based on NGSIM to extract the data of lane-change-related variables. Then, a lane-changing recognition model based on gridsearch-PSO is proposed. In the Matlab environment, the model has a test accuracy of 97.68%, while the SVM model without optimization parameters has a recognition accuracy of only 80.87%. The results show that the model has strong classification ability and robustness. Finally, by using the polynomial model for lateral movement trajectory fitting, K-fold cross-validation method is used for fitting polynomial model fitting test.

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“…However, the simulation also assumed the obstacle information was completely known, and only one obstacle was considered. Wu et al [16] presented a lanechange algorithm based on the PF that generates human-like trajectories, taking into account risks, drivers' focus shifts, and speed requirements, but their approach still required all obstacle information needs to be known in advance.…”

Section: Related Workmentioning

confidence: 99%

“…In the simulation study, we have conducted three comparative path planners to illustrate the performance of the proposed method: (i) PF-based path planner with constant speed (PF-CS) [15]; (ii) PF-based path planner with speed planning (PF-SP) [16]; (iii) Proposed PF-based occlusionaware path planner (PF-OAPP).…”

Section: A Simulation Settingsmentioning

confidence: 99%

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

Lin

Tsukada

2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.

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Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Cited by 23 publications

References 57 publications

Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Support Vector Machine Based Lane-Changing Behavior Recognition and Lateral Trajectory Prediction

Cooperative Path Planning Using Responsibility-Sensitive Safety (RSS)-based Potential Field with Sigmoid Curve

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