Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving

Palatti, Jiyo; Aksjonov, Andrei; Alcan, Gökhan; Kyrki, Ville

doi:10.1109/itsc48978.2021.9564499

Cited by 17 publications

(14 citation statements)

References 19 publications

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“…• State machines: Finite State Machines (FSM) model the behavior of a system by representing the system states with actions or conditions, avoiding the declaration of a vast number of rules. Palatti et al [24] targeted safe overtaking trajectories by combining a rulebased maneuver planner using Finite State Machines and reachable sets. A predictive maneuver-planning method for navigation in public highway traffic was proposed in [25].…”

Section: ) Rule Based Approachesmentioning

confidence: 99%

“…This state machine implemented the interaction protocols for the different scenarios (merging on highways, intersection crossing, and giving free passage to an emergency vehicle on highways). Recently, a maneuver planner based on finite state machines was used in [24] to seek safe overtaking maneuvers with aborting capabilities. A finite state machine based on heuristic rules is used to select an appropriate maneuver (lane keeping, overtaking or aborting), and a combination of reachable sets is used to generate intermediate reference targets based on the current maneuver.…”

Section: ) Rule Based Approachesmentioning

confidence: 99%

“…Motion planning approaches can be classified based on different criteria. In the following sections we present two dif- Rule-based approaches [17], [24], [25], [26] Logical-constrains for lane changes; Finite State Machines for overtaking maneuvers; predictive lane and reference speed selection state machine in highway traffic; state machine for merging on highway, intersection crossing and give free passage to emergency vehicles [17], [24], [25], [26] [17], [26] [17], [24], [25], [26] Utility-based approaches [21], [15], [16] Cost function for lane change decision making; receding-horizon multi-objective optimization for automatic parallel parking; fused decision making and trajectory planning with cost function deciding on maneuver-based tactical patterns; Utility function for mandatory, discretionary and anticipatory lane change decisions [15], [16], [17] [17] [17] [15], [16], [17] Probabilisticbased approaches [18], [27], [28], [19], [20] POMDP for uncertain environments; POMDP based on Monte-Carlo tree search for decision-making in highways; probabilistic model for lane change maneuver in highways; POMDP with discrete Bayesian network for decision making in dynamic and uncertain unsignalized urban environments; online POMDP considering uncertainty on intersections.…”

Section: Operational Level (Motion Planning)mentioning

confidence: 99%

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Review of Decision-Making and Planning Approaches in Automated Driving

Garrido

Resende

2022

IEEE Access

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The number of research papers on decision-making systems in automated driving has increased significantly over the last few years. Decision-making for automated driving can be performed at different levels: (i) strategic level: generating the optimal route up to the destination; (ii) tactical level: identifying and ranking feasible high-level maneuvers that the vehicle can perform, considering the dynamic objects that are in the surroundings; (iii) operational level: generating a collision-free trajectory (path and speed profile) up to the planning horizon; (iv) stability level: computing the motion control commands for tracking the trajectory. Additionally, supervision can be understood as a combination of one or more decision-making levels. Previous reviews have focused either on one of the levels of decision-making or on a specific environment where the approaches are applied, without any distinction between the contexts in which they are applied (robotics, unmanned vehicles or automated driving). This review studies the state-of-the-art on the decision-making approaches applied specifically to automated driving, during the last lustrum.

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Section: ) Rule Based Approachesmentioning

confidence: 99%

Section: ) Rule Based Approachesmentioning

confidence: 99%

Section: Operational Level (Motion Planning)mentioning

confidence: 99%

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Review of Decision-Making and Planning Approaches in Automated Driving

Garrido

Resende

2022

IEEE Access

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“…In self‐driving vehicles, overtaking trajectories are computed in planning modules by decision‐making algorithms. Different types of decision‐making algorithms are available in the literature, such as binary decision diagrams (Claussmann et al, 2015), learning‐based technologies (Liu et al, 2019, 2020; Mo et al, 2021) model predictive control (MPC), and nonlinear MPC (Palatti et al, 2021; Viana et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

Practical path planning techniques in overtaking for autonomous shuttles

Malayjerdi

Sell

Malayjerdi

et al. 2022

Journal of Field Robotics

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This paper proposes a reliable optimized sigmoid‐based path planning algorithm that ensures smooth, fast and safe overtaking maneuver, while maintaining the necessary safety distance. In the proposed method, the desired smoothness of trajectories, the changes in steering angle and the lateral acceleration are controlled in a robust way. This paper describes the simulations, and the confirming real‐world experiments, conducted using the autonomous shuttle iseAuto. Our results suggest that the sigmoid A‐star algorithm leads to a smoother and more reliable motion when compared to other two standard methods. Specifically, the abruptness of necessary steering angle changes is reduced by factor of 4, and approaching the level of an experienced driver‐like maneuver.

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“…One of the most relevant examples are lane changing and overtaking scenarios where, among other parts of the environment, traffic of the current and the adjacent lanes must be considered to find potential time slots and trajectories to pass a vehicle or obstacle in front while satisfying kinematic and safety constraints [172]. Most of the recently proposed ways for controlling an AD vehicle use methods like potential fields, cell decomposition, optimal control or model predictive control [536]. As these strategies are real-time optimization problems, i.e., they do not fall in the category of deep learning, the above mentioned network-based deep transfer learning is not applicable here.…”

Section: Applicationsmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

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Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving

Cited by 17 publications

References 19 publications

Review of Decision-Making and Planning Approaches in Automated Driving

Review of Decision-Making and Planning Approaches in Automated Driving

Practical path planning techniques in overtaking for autonomous shuttles

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

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