Universally Safe Swerve Maneuvers for Autonomous Driving

Iaco, Ryan De; Smith, Stephen L.; Czarnecki, Krzysztof

doi:10.1109/ojits.2021.3138953

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…Initial insights on how it can be defined can be found in [66]. As also stated in [67], [68], it is important to keep improving the safety aspects of intelligent transportation systems as we move forward into the next generation of intelligent vehicles.…”

Section: B Main Challengesmentioning

confidence: 99%

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis

Papadimitriou

2023

IEEE Open J. Intell. Transp. Syst.

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This study reviews the Artificial Intelligence and Machine Learning approaches developed thus far for driver profile and driving pattern recognition, representing a set of macroscopic and microscopic behaviors respectively, to enhance the understanding of human factors in road safety, and therefore reduce the number of crashes. It provides a definition of the two scientific fields in terms of safety, and identifies the most efficient approaches used regarding methodology, data collection and driving metrics. Results show that K-means and Neural Networks are the most commonly used methodologies for driver profile identification, and Dynamic Time Warping for driving pattern detection. Most studies discovered driver profiles related to aggressiveness, considering mainly speed and acceleration as driving metrics. Based on the gaps and challenges identified, this paper provides a new framework for combining microscopic and macroscopic driving behavior analysis, instead of examining them separately as is the state-of-theart. Such combined results can potentially improve the development of traffic risk models, which could be exploited in applications that monitor drivers in real-time and provide feedback. These models will represent human behavior more accurately, which can eventually lead to the recognition of "optimal" human driving patterns that Automated Vehicles (AV) could 'mimic' to become safer.

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Section: B Main Challengesmentioning

confidence: 99%

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Tselentis

Papadimitriou

2023

IEEE Open J. Intell. Transp. Syst.

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“…RSS is widely used to control swerve manoeuvres for autonomous driving. The authors in [14] propose a feasible solution using RSS to swerve manoeuvre boundary condition problems in addition to standard brake manoeuvres. For that reason, they build both lateral and longitudinal safe distances between two vehicles.…”

Section: Decision‐making Layer For Avsmentioning

confidence: 99%

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Abdallaoui,

Ikaouassen,

Kribèche

et al. 2023

The Journal of Engineering

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This abstract discusses the significant progress made in autonomous vehicles, focusing on decision‐making systems and control algorithms. It explores recent advances, challenges, and contributions in the field, emphasizing the need for precise navigation and control. The paper covers various methodologies, including rule‐based methods, machine learning, deep learning, probabilistic approaches, and hybrid approaches, examining their applications and effectiveness in ensuring safe navigation. Additionally, it reviews ongoing research efforts, emerging trends, and persistent challenges related to decision‐making and manoeuvre execution in autonomous vehicles, addressing complex topics such as sensor measurement uncertainty, dynamic environment modelling, real‐time responsiveness, and safe interactions with other road users. The objective is to provide a comprehensive overview of the state of the art in autonomous vehicle navigation and control for readers.

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“…RSS is widely used to control swerve maneuvers for autonomous driving. [6] Proposes a feasible solution using RSS to swerve maneuver boundary condition problems in addition to standard brake maneuvers. For that reason, they build both lateral and longitudinal safe distances between two vehicles.…”

Section: Requirementsmentioning

confidence: 99%

“…Fuzzy Analytic Hierarchy Process [3] Handles the uncertainty, robustness and flexibility, powerful in multi-objective decision-making, online ------------Responsibility Sensitive Safety [6] Red-Black Half Sweep Successive Over Relaxation [7] Guarantees safety Reduces computational complexity, fast iterative method, and solves path planning problems.…”

Section: Traditionalmentioning

confidence: 99%

Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

Abdallaoui

Ikaouassen

Kribèche

et al. 2023

Preprint

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As self-driving cars perform more tasks, new challenges arise. One of these challenging tasks is autonomous driving decision-making due to the uncertainty of the vehicle’s complex environment. This paper provides an overview of decision-making technology and trajectory control for autonomous vehicles. The main common goal in decision-making is to consider uncertainties, unpredictable situations, and driving tasks to propose a global and robust solution adapted to each situation. The main concern is safety. Decision-making falls into three categories. The first is the traditional approach, which often consists of building a rule system and deriving optimal operations. The advantages of such an approach are well known for being easy to understand and applicable to small problems. The second category of decision-making is based on a probabilistic process and, due to its efficiency, has several applications in this area. The third category is learning-based approaches. Once a decision has been made, manipulate the steering angle or accelerator/brake pedals to perform the appropriate action. Two approaches are existing to designing autonomous driving controllers. Either based on imitating human drivers that includes approaches based on the use of driver models such as AI, or the use of approach-based models

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Universally Safe Swerve Maneuvers for Autonomous Driving

Cited by 10 publications

References 20 publications

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Driver Profile and Driving Pattern Recognition for Road Safety Assessment: Main Challenges and Future Directions

Advancing autonomous vehicle control systems: An in‐depth overview of decision‐making and manoeuvre execution state of the art

Autonomous Vehicle Control Systems- State of the Art of Decision-Making and Maneuver execution

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