Planning and Decision-Making for Autonomous Vehicles

Schwarting, Wilko; Alonso–Mora, Javier; Rus, Daniela

doi:10.1146/annurev-control-060117-105157

Cited by 709 publications

(434 citation statements)

References 124 publications

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“…The goal in IL (Grigorescu et al, ; Rehder et al, ; Sun et al, ) is to learn the behavior of a human driver from recorded driving experiences (Schwarting, Alonso‐Mora, & Rus, ). The strategy implies a vehicle teaching process from human demonstration.…”

Section: Deep Learning For Path Planning and Behavior Arbitrationmentioning

confidence: 99%

“…Following, we discuss two of the most representative deep learning paradigms for path planning, namely IL (Grigorescu, Trasnea, Marina, Vasilcoi, & Cocias, 2019; Rehder, Quehl, & Stiller, 2017; Sun, Peng, Zhan, & Tomizuka, 2018) and DRL-based planning(Paxton, Raman, Hager, & Kobilarov, 2017;L. Yu, Shao, Wei, & Zhou, 2018).The goal in ILRehder et al, 2017;Sun et al, 2018) is to learn the behavior of a human driver from recorded driving experiences(Schwarting, Alonso-Mora, & Rus, 2018). The strategy implies a vehicle teaching process from human demonstration.…”

mentioning

confidence: 99%

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A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,102

495

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The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

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Section: Deep Learning For Path Planning and Behavior Arbitrationmentioning

confidence: 99%

mentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,102

495

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“…In this section, we review several learning‐based motion planning methods and decision‐making algorithms for autonomous vehicles and other intelligent agents. We refer interested readers to additional review articles include [KQCD15, PČY*16, SAMR18] for further reading.…”

Section: Applications In Autonomous Drivingmentioning

confidence: 99%

“…Virtual reality‐based driving training programs have helped new drivers to improve driving skills by producing realistic traffic environments [VRd18, LWX*18]. Traffic simulation can also be used as an effective tool for generating various traffic conditions for training and testing autonomous vehicles [SAMR18].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Chao

et al. 2019

Computer Graphics Forum

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Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.

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“…Research in robotics and artificial intelligence has found that planning ahead, in an online fashion, in our typically uncertain environment is a hard problem for artificial agents, (e.g., Kurniawati et al 2011). Even for mundane activities such as safely driving a car through typical traffic, artificial planning performance is currently well below human routine performance (for a current review see Schwarting, Alonso-Mora, and Rus 2018). Here, planning is required because a car responds rather slowly to one's actions so that one must predict the consequences of one's own actions into the future for at least a few seconds or even longer, especially in the presence of other traffic participants, whose behaviour must also be predicted.…”

Section: Planning In Uncertain Environmentsmentioning

confidence: 99%

Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales

Marković

Goschke

Kiebel

2019

Preprint

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Cognitive control is typically understood as a set of mechanisms which enable humans to reach goals that require integrating the consequences of actions over longer time scales. Importantly, using routine beheavior or making choices beneficial only at a short time scales would prevent one from attaining these goals. During the past two decades, researchers have proposed various computational cognitive models that successfully account for behaviour related to cognitive control in a wide range of laboratory tasks. As humans operate in a dynamic and uncertain environment, making elaborate plans and integrating experience over multiple time scales is computationally expensive, the specific question of how uncertain consequences at different time scales are integrated into adaptive decisions remains poorly understood. Here, we propose that precisely the problem of integrating experience and forming elaborate plans over multiple time scales is a key component for better understanding how human agents solve cognitive control dilemmas such as the exploration-exploitation dilemma. In support of this conjecture, we present a computational model of probabilistic inference over hidden states and actions, which are represented as a hierarchy of time scales. Simulations of goal-reaching agents instantiating the model in an uncertain and dynamic task environment show how the exploration-exploitation dilemma may be solved by inferring meta-control states which adapt behaviour to changing contexts.

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Planning and Decision-Making for Autonomous Vehicles

Cited by 709 publications

References 124 publications

A survey of deep learning techniques for autonomous driving

A survey of deep learning techniques for autonomous driving

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales

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