Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: A state-of-the-art survey

Sharma, Omveer; Sahoo, Niranjan; Puhan, Niladri B.

doi:10.1016/j.engappai.2021.104211

Cited by 80 publications

(38 citation statements)

References 102 publications

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“…Behavior planning is one of the most concerned fields in automatic driving [35]. This aspect can make autonomous vehicles drive safely and efficiently.…”

Section: Related Workmentioning

confidence: 99%

A Deep Coordination Graph Convolution Reinforcement Learning for Multi-Intelligent Vehicle Driving Policy

Tan

Zuo

2022

Wireless Communications and Mobile Computing

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With the growing up of Internet of Things technology, the application of Internet of Things has been popularized in the field of intelligent vehicles. Therefore, more artificial intelligence algorithms, especially DRL methods, are more widely used in autonomous driving. A large number of deep reinforcement learning (RL) technologies are continuously applied to the behavior planning module of single-vehicle autonomous driving in early. However, autonomous driving is an environment where multi-intelligent vehicles coexist, interact with each other, and dynamically change. In this environment, multiagent RL technology is one of the most promising technologies for solving the coordination behavior planning problem of multivehicles. However, the research related to this topic is rare. This paper introduces a dynamic coordination graph (CG) convolution technology for the cooperative learning of multi-intelligent vehicles. This method dynamically constructs a CG model among multiple vehicles, effectively reducing the impact of unrelated intelligent vehicles and simplifying the learning process. The relationship between intelligent vehicles is refined using the attention mechanism, and the graph convolution RL technology is used to simulate the message-passing aggregation algorithm to maximize the local utility and obtain the maximum joint utility to guide coordination learning. Driving samples are used as training data, and the model guided by reward shaping is combined with the model of the free graph convolution RL method, which enables our proposed method to achieve high gradualness and improve its learning efficiency. In addition, as the graph convolutional RL algorithm shares parameters between agents, it can easily build scales that are suitable for large-scale multiagent systems, such as traffic environments. Finally, the proposed algorithm is tested and verified for the multivehicle cooperative lane-changing problem in the simulation environment of autonomous driving. Experimental results show that our proposed method has better value function representation in that it can learn better coordination driving policies than traditional dynamic coordination algorithms.

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“…Behavior planning is one of the most concerned fields in automatic driving [35]. This aspect can make autonomous vehicles drive safely and efficiently.…”

Section: Related Workmentioning

confidence: 99%

A Deep Coordination Graph Convolution Reinforcement Learning for Multi-Intelligent Vehicle Driving Policy

Tan

Zuo

2022

Wireless Communications and Mobile Computing

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“…Many works can be found in the literature dealing with the design of controllers for trajectory following [ 12 ]. Among of all the robust controllers, the sliding mode law is worldwide recognized as one of the most effective to reject external matched perturbations [ 13 ], so they can be used to reject perturbations that affect road vehicles.…”

Section: Introductionmentioning

confidence: 99%

Driver Assisted Lane Keeping with Conflict Management Using Robust Sliding Mode Controller

Perozzi

Oudainia

Sentouh

et al. 2022

Sensors

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Lane-keeping assistance design for road vehicles is a multi-objective design problem that needs to simultaneously maintain lane tracking, ensure driver comfort, provide vehicle stability, and minimize conflict between the driver and the autonomous controller. In this work, a cooperative control strategy is proposed for lane-keeping keeping by integrating driving monitoring, variable level of assistance allocation, and human-in-the-loop control. In the first stage, a time-varying physical driver loading pattern is identified based on a relationship between lateral acceleration, road curvature, and the measured maximum driver torque. Together with the monitored driver state that indicates driver mental loading, an adaptive driver activity function is then formulated that replicates the levels of assistance required for the driver in the next stage. To smoothly transition authority between various modes (from manual to autonomous and vice versa) based on the generated levels of assistance, a novel higher-order sliding mode controller is proposed and closed-loop stability is established. Further, a novel sharing parameter (which is proportional to the torques coming from the driver and from the autonomous controller) is used to minimize the conflict. Experimental results on the SHERPA high-fidelity vehicle simulator show the real-time implementation feasibility. Extensive experimental results provided on the Satory test track show improvement in cooperative driving quality by 9.4%, reduction in steering workload by 86.13%, and reduced conflict by 65.38% when compared with the existing design (no sharing parameter). These results on the cooperative performance highlight the significance of the proposed controller for various road transportation challenges.

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“…In general, architectures for autonomous vehicles consist of three sub-systems: Perception, Planning and Control (Sharma et al, 2021). The approaches for implementing the Perception sub-system can be mainly divided subsequently into three categories: camera-based, LiDAR-based and sensor fusion-based approaches (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Looming from LiDAR

Yepes¹,

Raviv²

2022

Preprint

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Looming, traditionally defined as the relative expansion of objects in the observer's retina, is a fundamental visual cue for perception of threat and can be used to accomplish collision free navigation. The measurement of the looming cue is not only limited to vision, and can also be obtained from range sensors like LiDAR (Light Detection and Ranging). In this article we present two methods that process raw LiDAR data to estimate the looming cue. Using looming values we show how to obtain threat zones for collision avoidance tasks. The methods are general enough to be suitable for any six-degree-of-freedom motion and can be implemented in real-time without the need for fine matching, point-cloud registration, object classification or object segmentation. Quantitative results using the KITTI dataset shows advantages and limitations of the methods.

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Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: A state-of-the-art survey

Cited by 80 publications

References 102 publications

A Deep Coordination Graph Convolution Reinforcement Learning for Multi-Intelligent Vehicle Driving Policy

A Deep Coordination Graph Convolution Reinforcement Learning for Multi-Intelligent Vehicle Driving Policy

Driver Assisted Lane Keeping with Conflict Management Using Robust Sliding Mode Controller

Estimation of Looming from LiDAR

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