Decision Making Process of Autonomous Vehicle with Intention-Aware Prediction at Unsignalized Intersections

Hsu, Tsung-Ming; Chen, Yu-Rui; Wang, Cheng-Hsien

doi:10.1109/cacs50047.2020.9289815

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The experimental outcomes demonstrated that the suggested framework performed better at predicting driving intentions than logistic regression. A human-like decision model for unsignalized intersections was proposed by Hsu et al [ 55 ] by leveraging the intention-aware method to forecast the intentions of other drivers and the movement of obstacles based on a convolution neural network (CNN) with multiple objects tracking and Kalman-Filter operations. Another study by Song et al [ 56 ] implemented an intention-aware decision-making algorithm for an urban environment comprised of an uncontrolled intersection.…”

Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

How Do Autonomous Vehicles Decide?

Malik

Khan

El-Sayed

et al. 2022

Sensors

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The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.

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Section: The Analyses Of Decision-making Relevant Solutions For Auton...mentioning

confidence: 99%

How Do Autonomous Vehicles Decide?

Malik

Khan

El-Sayed

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, Galceran et al [24] proposed a synthesis reasoning and decision-making method in autopilot mode. CNN detection and Kalman filtering are used to predict the movement intention of obstacles as the basis for human-like, decision-making strategies [25]. This enhances the interaction between intelligent land vehicles and other drivers.…”

Section: Related Workmentioning

confidence: 99%

A Collision Relationship-Based Driving Behavior Decision-Making Method for an Intelligent Land Vehicle at a Disorderly Intersection via DRQN

Huo

et al. 2022

Sensors

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An intelligent land vehicle utilizes onboard sensors to acquire observed states at a disorderly intersection. However, partial observation of the environment occurs due to sensor noise. This causes decision failure easily. A collision relationship-based driving behavior decision-making method via deep recurrent Q network (CR-DRQN) is proposed for intelligent land vehicles. First, the collision relationship between the intelligent land vehicle and surrounding vehicles is designed as the input. The collision relationship is extracted from the observed states with the sensor noise. This avoids a CR-DRQN dimension explosion and speeds up the network training. Then, DRQN is utilized to attenuate the impact of the input noise and achieve driving behavior decision-making. Finally, some comparative experiments are conducted to verify the effectiveness of the proposed method. CR-DRQN maintains a high decision success rate at a disorderly intersection with partially observable states. In addition, the proposed method is outstanding in the aspects of safety, the ability of collision risk prediction, and comfort.

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