Towards human-like speed control in autonomous vehicles: A mountainous freeway case

Chen, Zhigui; Wang, Xuesong; Guo, Qiming; Tarko, Andrew P.

doi:10.1016/j.aap.2022.106566

Cited by 8 publications

(4 citation statements)

References 43 publications

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“…The slope change of the following section 1.93226 1.93277 0.00 9.00 1 The difference in curvature between the preceding segment of the combined curve and the current combined curve. 2 The difference in slope between the preceding segment of the combined curve and the current combined curve. 3 The difference in curvature between the following segment of the combined curve and the current combined curve.…”

Section: Geometric Datamentioning

confidence: 99%

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Wang,

Wei,

Wang

2024

Applied Sciences

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The free-flowing traffic environment of the freeway is an important application scenario for automatic driving. In this scenario, the freeway’s geometric design is an important factor because no other vehicle affects the driving process of the target vehicle. The freeway’s combined curves have more safety problems, but there are no quantitative guidelines for their geometric design. They present more challenges for automatic driving or driver assistance functions. If the relationship between human-drivers’ micro-behavior and the geometric design of combined curves is examined, it could provide theoretical support for the enhancement of automated driving and driver assistance functions as well as the quantitative design of combined curves. The paper analyzed the speed change and lane departure behaviors of combined curves, considering downslope curves, upslope curves, sag curves, and crest curves. The relationship between micro-driving behaviors and combined curves’ geometric design were determined using random forest models. The SHAP values of each variable were calculated. The results showed that (1) on a downslope curve and sag curve the speed change behavior should be paid more attention; on an upslope curve and crest curve, the lane departure behavior should be paid more attention; (2) the priority of geometric design parameters for four types of combined curves were different. Based on the results, drivers and autonomous vehicles can pay different levels of attention to their speed change and departure behavior on different combination curves, and take targeted improvement measures in time according to the driving status of the vehicles. Road designers can also prioritize more important road design parameters in the design process to avoid serious accidents caused by excessive speed changes and departures.

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Section: Geometric Datamentioning

confidence: 99%

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Wang,

Wei,

Wang

2024

Applied Sciences

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“…For example, Yan et al (2022) and Yang et al (2021) study the individualized driving behaviors of different drivers during lane‐changing scenarios to establish personalized driving preferences and steering characteristics. Li et al (2022) focuses on learning longitudinal speed planning for distinct driving styles, while Chen et al (2022) clusters driving data to identify three driving styles for speed control strategies. Additionally, Graf et al (2018) learns and defines two driving styles, steady and aggressive, to generate reference trajectories for planning purposes.…”

Section: Related Workmentioning

confidence: 99%

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

Wang,

Gong,

Gong

et al. 2024

Journal of Field Robotics

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Driving in an off‐road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human‐like cognition and cost evaluation for off‐road driving. First, we construct a multilayer map describing different features of off‐road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network‐long short‐term memory network to learn the trajectories planned by human drivers in various off‐road scenarios. Then, based on human‐like generated trajectories in different environments, we design a primitive‐based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off‐road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off‐road environment with complex terrain and varying road conditions. The experimental results show that the proposed human‐like motion planner has excellent adaptability to different off‐road conditions. It shows real‐time operation, greater stability, and more human‐like planning ability in diverse and challenging scenarios.

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“…As a result, consistent traffic speeds are preferred in order to lower the likelihood of accidents. For instance, it is anticipated that driverless cars will have skills far greater than those of human drivers [38]. Because these AVs are able to follow the speed values in a much more effective way than human drivers, the substantial variations in speed that now exist between vehicles may be decreased, which would make traffic much safer; Park and ride system (C 6 )…”

Section: Velocity Of Autonomous Vehicles (C 5 )mentioning

confidence: 99%

An Interactive Multi-Criteria Decision-Making Approach for Autonomous Vehicles and Distributed Resources Based on Logistic Systems: Challenges for a Sustainable Future

Gamal,

Abdel-Basset,

Hezam

et al. 2023

Sustainability

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The autonomous vehicle (AV) is one of the emerging technologies of the new age that has the potential to restructure transportation infrastructure. AVs are able to sense their surroundings and move around with control and self-sufficiency. AVs can contribute towards reducing traffic congestion on the roads, improving the quality of life, and achieving the highest levels of traffic safety. Thus, this type of vehicle can be integrated into the logistics industry. Due to the presence of several AVs, selecting a standard and efficient AV for logistics planning is a great challenge. The selection of an AV depends on many conflicting and essential criteria. Given its efficiency and reliability in dealing with conflicting criteria, a comprehensive multi-criteria decision-making (MCDM) approach was applied to solve the problem of selecting the optimal AV. However, the MCDM selection process is based on human judgment, which can be ambiguous. Accordingly, uncertainty was handled using type-2 neutrosophic numbers (T2NN). Initially, the method based on the removal effects of criteria (MEREC) was extended under T2NN and employed to assess and prioritize criteria. Then, the combined compromise solution (CoCoSo) method was extended under T2NN and applied to rank the candidate substitutions. To confirm the feasibility of the applied approach, an illustrative case study of four AVs was introduced. A sensitivity analysis was performed by changing the weights of the criteria and some other parameters to confirm the validity and stability of the proposed approach. In addition, a comparison analysis with other MCDM approaches was conducted to show the effectiveness and reliability of the applied approach. This research provides useful information for policymakers in the field of logistics. Finally, the results indicate that the velocity of AVs criterion is the most influential criterion in the selection of an intelligent AV.

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Towards human-like speed control in autonomous vehicles: A mountainous freeway case

Cited by 8 publications

References 43 publications

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Investigating Micro-Driving Behavior of Combined Horizontal and Vertical Curves Using an RF Model and SHAP Analysis

Motion planning for off‐road autonomous driving based on human‐like cognition and weight adaptation

An Interactive Multi-Criteria Decision-Making Approach for Autonomous Vehicles and Distributed Resources Based on Logistic Systems: Challenges for a Sustainable Future

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