Open Access
Autonomous Driving: Context and State-of-the-Art
Javier Ibanez-Guzman,Christian Laugier,John-David Yoder,Sebastian Thrun +3 more
- Vol. 2, pp 1271-1310
TLDR
In order to provide an overall perspective on how technology is converging towards vehicles with autonomous capabilities, advances have been classified into driver centric, network centric and vehicle centric.Abstract:
Vehicles are evolving into autonomous mobile-connected platforms. The rationale resides on the political and economic will towards a sustainable environment as well as advances in information and communication technologies that are rapidly being introduced into modern passenger vehicles. From a user perspective, safety and convenience are always a major concern. Further, new vehicles should enable people to drive that presently can not as well as to facilitate the continued mobility of the aging population. Advances are led by endeavors from vehicle manufacturers, the military and academia and development of sensors applicable to ground vehicles. Initially, the motivators are detailed on the reasons that vehicles are being built with intelligent capabilities. An outline of the navigation problem is presented to provide an understanding of the functions needed for a vehicle to navigate autonomously. In order to provide an overall perspective on how technology is converging towards vehicles with autonomous capabilities, advances have been classified into driver centric, network centric and vehicle centric. Vehicle manufacturers are introducing at a rapid pace Advanced Driving Assistance Systems; these are considered as Driver Centric with all functions facilitating driver awareness. This has resulted on the introduction of perception sensors utilizable in traffic situations and technologies that are advancing from simple (targeted to inform drivers) towards the control of the vehicle. The introduction of wireless links onboard vehicles should enable the sharing of information and thus enlarge the situational awareness of drivers as the perceived area is enlarged. Network Centric vehicles provide the means to perceive areas that vehicle onboard sensors alone can not observe and thus grant functions that allow for the deployment of vehicles with autonomous capabilities. Finally, vehicle centric functions are examined; these apply directly to the deployment of autonomous vehicles. Efforts in this realm are not new and thus fundamental work in this area is included. Sensors capable to detect objects in the road network are identified as dictating the pace of developments. The availability of intelligent sensors, advanced digital maps, and wireless communications technologies together with the availability of electric vehicles should allow for deployment on public streets without any environment modification. Likely, there will first be self-driving cars followed by environment modifications to facilitate their deployment.read more
Citations
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Hierarchical Trajectory Planning of an Autonomous Car Based on the Integration of a Sampling and an Optimization Method
TL;DR: A hierarchical trajectory planning based on the integration of a sampling and an optimization method for urban autonomous driving is presented and the proposed algorithms of the sampling-based behavioral and optimization-based motion trajectory were evaluated.
Proceedings ArticleDOI
MAV urban localization from Google street view data
TL;DR: The success of the approach shows that the new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.
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Shared Control Driver Assistance System Based on Driving Intention and Situation Assessment
TL;DR: The results prove that the shared control driver assistance system can successfully help drivers to avoid obstacles and obtains great vehicle stability performance in different scenarios.
Proceedings ArticleDOI
Ontology-based context awareness for driving assistance systems
TL;DR: The proposed ontology allows for a coherent understanding of the interactions between the perceived entities and contextual data and can be used to improve the situation awareness of an ADAS (Advanced Driving Assistance System), by determining which entities are the most relevant for the subject vehicle navigation.
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The key technology toward the self-driving car
TL;DR: The four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed and the development trend of self- driving car is predicted.
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