Safe Occlusion-Aware Autonomous Driving via Game-Theoretic Active Perception

Zhang, Zixu; Fisac, Jaime F.

doi:10.15607/rss.2021.xvii.066

Cited by 23 publications

(5 citation statements)

References 44 publications

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“…Given the study's methodology, we haven't considered additional uncertainties such as sensor noise, road friction coefficients, or controller inaccuracies. While we haven't factored in uncertainties like perception [27], it's worth mentioning that our method primarily deals with static obstacles that limit the field of view, typically identified reliably by perception systems. The method works for occluded areas behind clearly visible static obstacles and given semantic information.…”

Section: Discussionmentioning

confidence: 99%

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Toward Safer Autonomous Vehicles: Occlusion-Aware Trajectory Planning to Minimize Risky Behavior

Trauth,

Moller,

Betz

2023

IEEE Open J. Intell. Transp. Syst.

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Autonomous vehicles face numerous challenges to ensure safe operation in unpredictable and hazardous conditions. The autonomous driving environment is characterized by high uncertainty, especially in occluded areas with limited information about the surrounding obstacles. This work aims to provide a trajectory planner to solve these unsafe environments. The work proposes an approach combining a visibility model, contextual environmental information, and behavioral planning algorithms to predict the likelihood of occlusions and collision probabilities. Ultimately, this allows us to estimate the potential harm from collisions with pedestrians in occluded situations. The primary goal of our proposed approach is to minimize the risk of hitting pedestrians and to establish a predefined, adjustable maximum level of harm. We show several practical applications for informing a sampling-based trajectory planner about occluded areas to increase safety. In addition, to respond to possible high-risk situations, we introduce an adjustable threshold that governs the vehicle's speed when encountering uncertain situations and strategies to maximize the vehicle's visible area. In implementing our novel methodology, we analyzed several realworld scenarios in a simulation environment. Our results indicate that combining occlusion-aware trajectory planning algorithms and harm estimation significantly influences vehicle driving behavior, especially in risky situations. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-Motion-Planner.

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Section: Discussionmentioning

confidence: 99%

“…New concepts and planning extensions: There is also work developing a new planning concept for the problem of occluded areas, for example, using game theory [27]. Zhang et al formulate the problem as a dynamic zero-sum game between the autonomous vehicle and an initially occluded road user.…”

Section: Related Workmentioning

confidence: 99%

Toward Safer Autonomous Vehicles: Occlusion-Aware Trajectory Planning to Minimize Risky Behavior

Trauth,

Moller,

Betz

2023

IEEE Open J. Intell. Transp. Syst.

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“…Inspired by works [34] and [35]we incorporate the Probability distribution of agent emergence from obscured regions predicted from contextual information which obtained in previous work [2] into the incomplete information virtual game to regulate the speed of the autonomous vehicle on motion planning.…”

Section: Related Wordmentioning

confidence: 99%

Occlusion-Aware Motion Planning for Autonomous Driving

Wang

Zhou

et al. 2023

IEEE Access

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Motion planning of autonomous vehicles is still challenging in urban road environments with occlusions. In this work, with the view of safety, comfort and efficiency, we present a motion planning framework that enables autonomous vehicles to navigate safely in urban road with occlusions. Our solution mainly includes three parts: local path planning, trajectory planning and speed planning. First, based on the improved Artificial Potential Field to generate the local path, then the optimal trajectory is solved in the S-L coordinate with the local path as the reference line. Finally, the potential risk probability of the occluded area is incorporated into the incomplete information static game framework and implement speed planning based on the game results and the proposed vehicle "safe driving" to complete the collision avoidance between the autonomous vehicle and visible or obscured dynamic traffic participants. Through simulation verification in common traffic scenarios and a comparison with some existing methods, the proposed method is proven to enable autonomous vehicles to navigate safely in traffic scenarios with occlusions and improve the driving efficiency and comfort of autonomous vehicles.

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“…However, there is still a long way to full L4 autonomous driving truck, especially dealing with more complex traffic and pedestrians. We noticed many recent advanced studies have been released targeting several areas in autonomous driving, e.g., collision avoidance (Zhang and Fisac, 2021), left-turn planning (Shu et al, 2020). For the future study, we plan to cover more logistic truck operation scenarios in addition to highways, e.g., on-ramp/off-ramp scenarios, left-turn/right-turn planning in crowd intersections.…”

Section: Conclusion and Future Studymentioning

confidence: 99%

An Intelligent Self-Driving Truck System for Highway Transportation

Wang

Gao²,

Lan³

et al. 2022

Front. Neurorobot.

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Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.

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Safe Occlusion-Aware Autonomous Driving via Game-Theoretic Active Perception

Cited by 23 publications

References 44 publications

Toward Safer Autonomous Vehicles: Occlusion-Aware Trajectory Planning to Minimize Risky Behavior

Toward Safer Autonomous Vehicles: Occlusion-Aware Trajectory Planning to Minimize Risky Behavior

Occlusion-Aware Motion Planning for Autonomous Driving

An Intelligent Self-Driving Truck System for Highway Transportation

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