Ofjall 2016a - Revision history

Scipediacontent at 14:34, 26 January 2021

2021-01-26T14:34:36Z

Scipediacontent: Scipediacontent moved page Draft Content 984304044 to Ofjall 2016a

2021-01-21T13:56:30Z

Scipediacontent moved page Draft Content 984304044 to Ofjall 2016a

Scipediacontent: Created page with " == Abstract == Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of..."

2021-01-21T13:56:28Z

Created page with " == Abstract == Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of..."

New page

== Abstract ==

Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of sensors has also decreased from the vehicles of the initial DARPA challenge, more resembling a pile of sensors with a car underneath. Still, anyone driving a tele-operated toy using a video link is a demonstration that a single camera provides enough information about the surronding world. Â Most lane assist systems are developed for highway use and depend on visible lane markers. However, lane markers may not be visible due to snow or wear, and there are roads without lane markers. With a slightly different approach, autonomous road following can be obtained on almost any kind of road. Using realtime online machine learning, a human driver can demonstrate driving on a road type unknown to the system and after some training, the system can seamlessly take over. The demonstrator system presented in this work has shown capability of learning to follow different types of roads as well as learning to follow a person. The system is based solely on vision, mapping camera images directly to control signals. Â Such systems need the ability to handle multiple-hypothesis outputs as there may be several plausible options in similar situations. If there is an obstacle in the middle of the road, the obstacle can be avoided by going on either side. However the average action, going straight ahead, is not a viable option. Similarly, at an intersection, the system should follow one road, not the average of all roads. Â To this end, an online machine learning framework is presented where inputs and outputs are represented using the channel representation. The learning system is structurally simple and computationally light, based on neuropsychological ideas presented by Donald Hebb over 60 years ago. Nonetheless the system has shown a cabability to learn advanced tasks. Furthermore, the structure of the system permits a statistical interpretation where a non-parametric representation of the joint distribution of input and output is generated. Prediction generates the conditional distribution of the output, given the input. Â The statistical interpretation motivates the introduction of priors. In cases with multiple options, such as at intersections, a prior can select one mode in the multimodal distribution of possible actions. In addition to the ability to learn from demonstration, a possibility for immediate reinforcement feedback is presented. This allows for a system where the teacher can choose the most appropriate way of training the system, at any time and at her own discretion. Â The theoretical contributions include a deeper analysis of the channel representation. A geometrical analysis illustrates the cause of decoding bias commonly present in neurologically inspired representations, and measures to counteract it. Confidence values are analyzed and interpreted as evidence and coherence. Further, the use of the truncated cosine basis function is motivated. Â Finally, a selection of applications is presented, such as autonomous road following by online learning and head pose estimation. A method founded on the same basic principles is used for visual tracking, where the probabilistic representation of target pixel values allows for changes in target appearance.

== Original document ==

The different versions of the original document can be found in:

* [http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT02 http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT02]

* [http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125916 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125916]

* [http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT02.pdf http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT02.pdf],
: [http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT01.pdf http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT01.pdf],
: [http://dx.doi.org/10.3384/diss.diva-125916 http://dx.doi.org/10.3384/diss.diva-125916] under the license https://creativecommons.org/licenses/by-nc/4.0/

* [http://liu.diva-portal.org/smash/record.jsf?pid=diva2%3A916645 http://liu.diva-portal.org/smash/record.jsf?pid=diva2%3A916645],
: [http://www.diva-portal.org/smash/record.jsf?pid=diva2:916645 http://www.diva-portal.org/smash/record.jsf?pid=diva2:916645],
: [http://core.ac.uk/display/36367814 http://core.ac.uk/display/36367814],
: [http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT02 http://liu.diva-portal.org/smash/get/diva2:916645/FULLTEXT02],
: [http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125916 http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125916],
: [https://trid.trb.org/view/1463228 https://trid.trb.org/view/1463228],
: [https://academic.microsoft.com/#/detail/2336032659 https://academic.microsoft.com/#/detail/2336032659]

← Older revision		Revision as of 14:34, 26 January 2021
Line 2:		Line 2:
	== Abstract ==		== Abstract ==

−	Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of sensors has also decreased from the vehicles of the initial DARPA challenge, more resembling a pile of sensors with a car underneath. Still, anyone driving a tele-operated toy using a video link is a demonstration that a single camera provides enough information about the surronding world. Â Most lane assist systems are developed for highway use and depend on visible lane markers. However, lane markers may not be visible due to snow or wear, and there are roads without lane markers. With a slightly different approach, autonomous road following can be obtained on almost any kind of road. Using realtime online machine learning, a human driver can demonstrate driving on a road type unknown to the system and after some training, the system can seamlessly take over. The demonstrator system presented in this work has shown capability of learning to follow different types of roads as well as learning to follow a person. The system is based solely on vision, mapping camera images directly to control signals. Â Such systems need the ability to handle multiple-hypothesis outputs as there may be several plausible options in similar situations. If there is an obstacle in the middle of the road, the obstacle can be avoided by going on either side. However the average action, going straight ahead, is not a viable option. Similarly, at an intersection, the system should follow one road, not the average of all roads. Â To this end, an online machine learning framework is presented where inputs and outputs are represented using the channel representation. The learning system is structurally simple and computationally light, based on neuropsychological ideas presented by Donald Hebb over 60 years ago. Nonetheless the system has shown a cabability to learn advanced tasks. Furthermore, the structure of the system permits a statistical interpretation where a non-parametric representation of the joint distribution of input and output is generated. Prediction generates the conditional distribution of the output, given the input. Â The statistical interpretation motivates the introduction of priors. In cases with multiple options, such as at intersections, a prior can select one mode in the multimodal distribution of possible actions. In addition to the ability to learn from demonstration, a possibility for immediate reinforcement feedback is presented. This allows for a system where the teacher can choose the most appropriate way of training the system, at any time and at her own discretion. Â The theoretical contributions include a deeper analysis of the channel representation. A geometrical analysis illustrates the cause of decoding bias commonly present in neurologically inspired representations, and measures to counteract it. Confidence values are analyzed and interpreted as evidence and coherence. Further, the use of the truncated cosine basis function is motivated. Â Finally, a selection of applications is presented, such as autonomous road following by online learning and head pose estimation. A method founded on the same basic principles is used for visual tracking, where the probabilistic representation of target pixel values allows for changes in target appearance.	+	Driver assistance systems in modern cars now show clear steps towards autonomous driving and improvements are presented in a steady pace. The total number of sensors has also decreased from the vehicles of the initial DARPA challenge, more resembling a pile of sensors with a car underneath. Still, anyone driving a tele-operated toy using a video link is a demonstration that a single camera provides enough information about the surronding world. Most lane assist systems are developed for highway use and depend on visible lane markers. However, lane markers may not be visible due to snow or wear, and there are roads without lane markers. With a slightly different approach, autonomous road following can be obtained on almost any kind of road. Using realtime online machine learning, a human driver can demonstrate driving on a road type unknown to the system and after some training, the system can seamlessly take over. The demonstrator system presented in this work has shown capability of learning to follow different types of roads as well as learning to follow a person. The system is based solely on vision, mapping camera images directly to control signals. Such systems need the ability to handle multiple-hypothesis outputs as there may be several plausible options in similar situations. If there is an obstacle in the middle of the road, the obstacle can be avoided by going on either side. However the average action, going straight ahead, is not a viable option. Similarly, at an intersection, the system should follow one road, not the average of all roads. To this end, an online machine learning framework is presented where inputs and outputs are represented using the channel representation. The learning system is structurally simple and computationally light, based on neuropsychological ideas presented by Donald Hebb over 60 years ago. Nonetheless the system has shown a cabability to learn advanced tasks. Furthermore, the structure of the system permits a statistical interpretation where a non-parametric representation of the joint distribution of input and output is generated. Prediction generates the conditional distribution of the output, given the input. The statistical interpretation motivates the introduction of priors. In cases with multiple options, such as at intersections, a prior can select one mode in the multimodal distribution of possible actions. In addition to the ability to learn from demonstration, a possibility for immediate reinforcement feedback is presented. This allows for a system where the teacher can choose the most appropriate way of training the system, at any time and at her own discretion. The theoretical contributions include a deeper analysis of the channel representation. A geometrical analysis illustrates the cause of decoding bias commonly present in neurologically inspired representations, and measures to counteract it. Confidence values are analyzed and interpreted as evidence and coherence. Further, the use of the truncated cosine basis function is motivated. Finally, a selection of applications is presented, such as autonomous road following by online learning and head pose estimation. A method founded on the same basic principles is used for visual tracking, where the probabilistic representation of target pixel values allows for changes in target appearance.

← Older revision	Revision as of 13:56, 21 January 2021
(No difference)