2017
DOI: 10.3390/machines5010006
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Abstract: Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehic… Show more

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Cited by 445 publications
(242 citation statements)
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References 267 publications
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“…Such structures learn discriminative features from data automatically, giving them the ability to approximate very complex nonlinear relationships (BOX 1). While most earlier AI methods have led to applications with subhuman performance, recent deep learning algorithms are able to match and even surpass humans in task-specific applications 25 (FIG. 1).…”
mentioning
confidence: 99%
“…Such structures learn discriminative features from data automatically, giving them the ability to approximate very complex nonlinear relationships (BOX 1). While most earlier AI methods have led to applications with subhuman performance, recent deep learning algorithms are able to match and even surpass humans in task-specific applications 25 (FIG. 1).…”
mentioning
confidence: 99%
“…The task of automated driving can be decomposed into the following subtasks: (i) defining the desired trajectory (driving line) and the desired pattern of speed along this trajectory, (ii) keeping the actual trajectory of the car as close as possible to the desired one, and (iii) maintaining the actual speed of the car along this trajectory as close as possible to the desired one [9,10]. We considered the first and third subtasks to be beyond the scope of our current work; instead, we focused on the second one-in challenging, slippery road conditions-which could be solved by an appropriate steering of the car.…”
Section: Introductionmentioning
confidence: 99%
“…We consider longterm reasoning as solved, as provided by navigation systems. These are standard methods for selecting a route through the road network, from the car's current position to destination (Pendleton et al, 2017). Learning-based control methods: Classical controllers make use of an a priori model composed of fixed parameters.…”
Section: Discussionmentioning
confidence: 99%
“…It addresses the taxonomy aspects of path planning, namely the mission planner, behavior planner, and motion planner. However, Pendleton et al () do not include a review on deep learning technologies, although the state‐of‐the‐art literature has revealed an increased interest in using deep learning technologies for path planning and behavior arbitration. Following, we discuss two of the most representative deep learning paradigms for path planning, namely IL (Grigorescu, Trasnea, Marina, Vasilcoi, & Cocias, ; Rehder, Quehl, & Stiller, ; Sun, Peng, Zhan, & Tomizuka, ) and DRL‐based planning (Paxton, Raman, Hager, & Kobilarov, ; L. Yu, Shao, Wei, & Zhou, ).…”
Section: Deep Learning For Path Planning and Behavior Arbitrationmentioning
confidence: 99%