Autonomous car decision making and trajectory tracking based on genetic algorithms and fractional potential fields

Receveur, Jean-Baptiste; Victor, Stéphane; Melchior, Pierre

doi:10.1007/s11370-020-00314-x

Cited by 31 publications

(8 citation statements)

References 33 publications

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“…In literature, GAs have been widely used in optimizing the solution to the lateral control problem of the AVS; in this regard, Son et al [86] have proposed a genetic algorithmbased driving decision strategy (DDS) approach to determine the optimal maneuver selection by considering the real-time internal (i.e., RPM and steering angle) and external factors such as road conditions and orientation of the objects surrounding the vehicle. In another research work, Receveur et al [87] have presented a GA-multicriteria potential field combined model-based autonomous steering control to determine the optimal global trajectory planning and local motion optimization of the vehicle simultaneously. e authors categorized the experiments into (i) potential field and (ii) GA potential Field and evaluated the performance of the proposed model in the simulated road environment.…”

Section: Evolutionary Methodsmentioning

confidence: 99%

“…In the literature, various optimization techniques have been employed to fine-tune the output of the steering control systems. References [86][87][88][89][90] have utilized the genetic algorithms to optimize the kinematical and dynamic model of the steering controller of the vehicle. Besides, references [22,[103][104][105][106][107][108][109][110][111][112][113][114][115][116] have used swarm optimization techniques to optimize the parameters of the steering control methods.…”

Section: Lack Of Validation Techniques For the Optimizationmentioning

confidence: 99%

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Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

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In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

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Section: Evolutionary Methodsmentioning

confidence: 99%

Section: Lack Of Validation Techniques For the Optimizationmentioning

confidence: 99%

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Rasib

Butt

Khalid

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Consequently, they are usable in a limited number of scenarios, by comparison. Some works attempt to circumvent this problem using evolutionary algorithms as multicriteria optimization methods [62]. However, while capable of covering a significant portion of the problem space, such methods have the downside that the optimal trajectory needs to be periodically recalculated, which can hinder performance especially for on-board-only systems.…”

Section: Other Techniquesmentioning

confidence: 99%

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

2021

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This paper provides a literature review of some of the most important concepts, techniques, and methodologies used within autonomous car systems. Specifically, we focus on two aspects extensively explored in the related literature: tracking, i.e., identifying pedestrians, cars or obstacles from images, observations or sensor data, and prediction, i.e., anticipating the future trajectories and motion of other vehicles in order to facilitate navigating through various traffic conditions. Approaches based on deep neural networks and others, especially stochastic techniques, are reported.

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“…A successful navigation system must be aware of obstacles scattered in the navigation environment at every move; thus, many studies proposed methods to avoid obstacles. The majority of these studies used common approaches in this field, some of which include the use of fuzzy systems [14][15][16], neural networks [17][18][19], and Virtual Potential Fields [20][21][22][23][24].…”

Section: Obstacle Avoidancementioning

confidence: 99%

Enhancing Robots Navigation in Internet of Things Indoor Systems

et al. 2021

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In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented and compared with the popular approach wall-following to pull the robot out of the local minima enclosure namely; Random Virtual Target, Reflected Virtual Target, Global Path Backtracking, Half Path Backtracking, and Local Path Backtracking. The proposed approaches mainly depend on changing the target location temporarily to avoid the original target’s attraction force effect on the robot. Moreover, to avoid getting trapped in the same location, a virtual obstacle is placed to cover the local minima enclosure. To include the most common shapes of deadlock situations, the proposed approaches were evaluated in four different environments; V-shaped, double U-shaped, C-shaped, and cluttered environments. The results reveal that the robot, using any of the proposed approaches, requires fewer steps to reach the destination, ranging from 59 to 73 m on average, as opposed to the wall-following strategy, which requires an average of 732 m. On average, the robot with a constant speed and reflected virtual target approach takes 103 s, whereas the identical robot with a wall-following approach takes 907 s to complete the tasks. Using a fuzzy-speed robot, the duration for the wall-following approach is greatly reduced to 507 s, while the reflected virtual target may only need up to 20% of that time. More results and detailed comparisons are embedded in the subsequent sections.

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Autonomous car decision making and trajectory tracking based on genetic algorithms and fractional potential fields

Cited by 31 publications

References 33 publications

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

Enhancing Robots Navigation in Internet of Things Indoor Systems

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