Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan

Cao, Liling; Feng, Xinxin; Liu, Junli; Zhou, Guofeng

doi:10.3390/app14041354

Applied Sciences

2024

DOI: 10.3390/app14041354

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Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan

Liling Cao,

Xinxin Feng,

Junli Liu

et al.

Abstract: The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is… Show more

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2024

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Cited by 3 publications

References 26 publications

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Study on the Deviation Characteristics of Driving Trajectories for Autonomous Vehicles and the Design of Dedicated Lane Widths

Cao,

Liao,

Lai

et al. 2024

Sustainability

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The vehicular trajectory offset represents a critical controlling element in the design of lane width. In light of the paucity of extant research on the lane widths for dedicated autonomous vehicle lanes, this study deployed the PreScan-Simulink co-simulation platform. Based on the established typical lateral and longitudinal control methods for autonomous vehicles, we initially identified the primary factors influencing trajectory offset through multifactorial coupled analysis. Subsequently, we conducted quantitative research on vehicle trajectory offset using S-shaped curves to elucidate the patterns in geometric elements’ impact on trajectory offset. Following this, we established a model of the relationship between design speed and trajectory offset under different vehicle types. Ultimately, we calculated the lane width values for scenarios involving varying positions and numbers of dedicated lanes. The results demonstrate that vehicle speed significantly impacts the trajectory offsets of autonomous vehicles. For passenger cars, the mean offset at speeds between 60 and 130 km/h is approximately 10 cm. At higher speeds of 140–150 km/h, the offset is more variable. The range of offset exhibited by trucks at speeds between 60 and 100 km/h is [8 cm, 16 cm]. In the case of a single dedicated lane, the width of the inner lanes intended for passenger cars is [2.60 m, 3.00 m], while the outer lanes designed to accommodate trucks have a width of [3.00 m, 3.20 m]. In scenarios with two dedicated lanes, the width of lanes for passenger cars can be reduced further, whereas the required lane width for trucks remains largely unchanged compared to that for single-lane setups. The conclusions show that the width of lanes adapted to autonomous vehicles could be reduced, which could help to optimize the use of road space, thus potentially reducing the occupation of land resources, reducing the environmental impact of road construction, and contributing to sustainable development. This study also provides valuable insights for the design of lanes dedicated to autonomous vehicles.

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Study on the Deviation Characteristics of Driving Trajectories for Autonomous Vehicles and the Design of Dedicated Lane Widths

Cao,

Liao,

Lai

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

A Testing and Evaluation Method for the Car-Following Models of Automated Vehicles Based on Driving Simulator

Zhang,

Shao,

Shi

et al. 2024

Systems

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The continuous advancement of connected and automated driving technologies has garnered considerable public attention regarding the safety and reliability of automated vehicles (AVs). Comprehensive and efficient testing is essential before AVs can be deployed on public roads. Current mainstream testing methods involve high costs in real-world settings and limited immersion in numerical simulations. To address these challenges and facilitate testing in mixed traffic scenarios involving both human-driven vehicles (HDVs) and AVs, we propose a testing and evaluation approach using a driving simulator. Our methodology comprises three fundamental steps. First, we systematically classify scenario elements by drawing insights from the scenario generation logic of the driving simulator. Second, we establish an interactive traffic scenario that allows human drivers to manipulate vehicles within the simulator while AVs execute their decision and planning algorithms. Third, we introduce an evaluation method based on this testing approach, validated through a case study focused on car-following models. The experimental results confirm the efficiency of the simulation-based testing method and demonstrate how car-following efficiency and comfort decline with increased speeds. The proposed approach offers a cost-effective and comprehensive solution for testing, considering human driver behavior, making it a promising method for evaluating AVs in mixed traffic scenarios.

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A path-planning algorithm for autonomous vehicles based on traffic stability criteria: the AS-IAPF algorithm

Zhao,

Li,

et al. 2024

Mech. Sci.

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Abstract. Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles.

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Automatic Generation System for Autonomous Driving Simulation Scenarios Based on PreScan

Cited by 3 publications

References 26 publications

Study on the Deviation Characteristics of Driving Trajectories for Autonomous Vehicles and the Design of Dedicated Lane Widths

Study on the Deviation Characteristics of Driving Trajectories for Autonomous Vehicles and the Design of Dedicated Lane Widths

A Testing and Evaluation Method for the Car-Following Models of Automated Vehicles Based on Driving Simulator

A path-planning algorithm for autonomous vehicles based on traffic stability criteria: the AS-IAPF algorithm

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