A Learning-Based Framework for Velocity Control in Autonomous Driving

Lefèvre, Stéphanie; Carvalho, Ashwin; Borrelli, Francesco

doi:10.1109/tase.2015.2498192

Cited by 182 publications

(99 citation statements)

References 18 publications

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“…motion sickness, see also Diels & Bos, 2016; apparent safety (the feeling of safe operation of the automated vehicle); natural, human-like paths]. A more recent study (Lefèvre, Carvalho, & Borrelli, 2016) developed a learning-based approach for automated vehicles with the aim to replicate human-like driving styles (i.e. velocity control).…”

Section: Literature Resultsmentioning

confidence: 99%

Policy and society related implications of automated driving: A review of literature and directions for future research

Milakis

Arem

Wee

2017

Journal of Intelligent Transportation Systems

725

383

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KEYWORDSautomated driving; first, second, and third order impacts; policy and societal implications; ripple effect ABSTRACTIn this paper, the potential effects of automated driving that are relevant to policy and society are explored, findings discussed in literature about those effects are reviewed and areas for future research are identified. The structure of our review is based on the ripple effect concept, which represents the implications of automated vehicles at three different stages: first-order (traffic, travel cost, and travel choices), second-order (vehicle ownership and sharing, location choices and land use, and transport infrastructure), and third-order (energy consumption, air pollution, safety, social equity, economy, and public health). Our review shows that first-order impacts on road capacity, fuel efficiency, emissions, and accidents risk are expected to be beneficial. The magnitude of these benefits will likely increase with the level of automation and cooperation and with the penetration rate of these systems. The synergistic effects between vehicle automation, sharing, and electrification can multiply these benefits. However, studies confirm that automated vehicles can induce additional travel demand because of more and longer vehicle trips. Potential land use changes have not been included in these estimations about excessive travel demand. Other third-order benefits on safety, economy, public health and social equity still remain unclear. Therefore, the balance between the short-term benefits and long-term impacts of vehicle automation remains an open question.

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Section: Literature Resultsmentioning

confidence: 99%

Policy and society related implications of automated driving: A review of literature and directions for future research

Milakis

Arem

Wee

2017

Journal of Intelligent Transportation Systems

725

383

View full text Add to dashboard Cite

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“…Learning techniques are commonly used to learn a dynamics model which in turn improves an a priori system model in iterative learning control (ILC; Kapania & Gerdes, ; Ostafew, Schoellig, & Barfoot, ; Panomruttanarug, ; Z. Yang, Zhou, Li, & Wang, ) and model predictive control (MPC; Drews, Williams, Goldfain, Theodorou, & Rehg, 2017; Lefevre, Carvalho, & Borrelli, ; Lefèvre, Carvalho, & Borrelli, ; Ostafew et al, , ; Pan et al, , ; Rosolia, Carvalho, & Borrelli, ).…”

Section: Motion Controllers For Ai‐based Self‐driving Carsmentioning

confidence: 99%

“…In previous works, learning controllers have been introduced based on simple function approximators, such as Gaussian process (GP) modeling (Meier, Hennig, & Schaal, 2014;Nguyen-Tuong, Peters, & Seeger, 2008;Ostafew et al, 2015;Ostafew, Schoellig, & Barfoot, 2016) or support vector regression (Sigaud, Salaün, & Padois, 2011 Lefèvre, Carvalho, & Borrelli, 2016;Ostafew et al, 2015Ostafew et al, , 2016Pan et al, 2018Pan et al, , 2017Rosolia, Carvalho, & Borrelli, 2017).…”

Section: Learning Controllersmentioning

confidence: 99%

“…Learning has been used in conjunction with MPC to learn driving models (Lefevre et al, 2015;Lefèvre et al, 2016), driving dynamics for race cars operating at their handling limits (Drews et al, 2017;Rosolia et al, 2017), as well as to improve path tracking accuracy (Brunner, Rosolia, Gonzales, & Borrelli, 2017;Ostafew et al, 2015Ostafew et al, , 2016. These methods use learning mechanisms to identify nonlinear dynamics that are used in the MPC's trajectory cost function optimization.…”

Section: Learning Controllersmentioning

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

1,214

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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“…Having examined briefly the major components that could be needed to achieve autonomous vehicle driving, it is worth mentioning that there is a great interest in the development of more complex strategies like those regarding cooperative autonomous driving [48] aiming to obtain intelligent vehicles that could share roads and streets [49][50][51]. More recently and regarding bus automation, a small electric Cyberbus was used to demonstrate a parametric-based path generation algorithm for roundabouts [52].…”

mentioning

confidence: 99%

An Experimental Platform for Autonomous Bus Development

et al. 2017

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Nowadays, with highly developed instrumentation, sensing and actuation technologies, it is possible to foresee an important advance in the field of autonomous and/or semi-autonomous transportation systems. Intelligent Transport Systems (ITS) have been subjected to very active research for many years, and Bus Rapid Transit (BRT) is one area of major interest. Among the most promising transport infrastructures, the articulated bus is an interesting, low cost, high occupancy capacity and friendly option. In this paper, an experimental platform for research on the automatic control of an articulated bus is presented. The aim of the platform is to allow full experimentation in real conditions for testing technological developments and control algorithms. The experimental platform consists of a mobile component (a commercial articulated bus) fully instrumented and a ground test area composed of asphalt roads inside the Consejo Superior de Investigaciones Científicas (CSIC) premises. This paper focuses also on the development of a human machine interface to ease progress in control system evaluation. Some experimental results are presented in order to show the potential of the proposed platform.

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A Learning-Based Framework for Velocity Control in Autonomous Driving

Cited by 182 publications

References 18 publications

Policy and society related implications of automated driving: A review of literature and directions for future research

Policy and society related implications of automated driving: A review of literature and directions for future research

A survey of deep learning techniques for autonomous driving

An Experimental Platform for Autonomous Bus Development

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