Autonomous driving: cognitive construction and situation understanding

Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Chen, Shitao [verfasserIn] Jian, Zhiqiang Huang, Yuhao Chen, Yu Zhou, Zhuoli Zheng, Nanning

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	autonomous driving event-driven mechanism cognitive construction situation understanding intuitive reasoning

Anmerkung:	© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Übergeordnetes Werk:	Enthalten in: Science in China - Heidelberg : Springer, 2001, 62(2019), 8 vom: 12. Juli
Übergeordnetes Werk:	volume:62 ; year:2019 ; number:8 ; day:12 ; month:07

Links:	Volltext

DOI / URN:	10.1007/s11432-018-9850-9

Katalog-ID:	SPR019330308

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allfields	10.1007/s11432-018-9850-9 doi (DE-627)SPR019330308 (SPR)s11432-018-9850-9-e DE-627 ger DE-627 rakwb eng Chen, Shitao verfasserin aut Autonomous driving: cognitive construction and situation understanding 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. autonomous driving (dpeaa)DE-He213 event-driven mechanism (dpeaa)DE-He213 cognitive construction (dpeaa)DE-He213 situation understanding (dpeaa)DE-He213 intuitive reasoning (dpeaa)DE-He213 Jian, Zhiqiang aut Huang, Yuhao aut Chen, Yu aut Zhou, Zhuoli aut Zheng, Nanning aut Enthalten in Science in China Heidelberg : Springer, 2001 62(2019), 8 vom: 12. Juli (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:62 year:2019 number:8 day:12 month:07 https://dx.doi.org/10.1007/s11432-018-9850-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 62 2019 8 12 07
spelling	10.1007/s11432-018-9850-9 doi (DE-627)SPR019330308 (SPR)s11432-018-9850-9-e DE-627 ger DE-627 rakwb eng Chen, Shitao verfasserin aut Autonomous driving: cognitive construction and situation understanding 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. autonomous driving (dpeaa)DE-He213 event-driven mechanism (dpeaa)DE-He213 cognitive construction (dpeaa)DE-He213 situation understanding (dpeaa)DE-He213 intuitive reasoning (dpeaa)DE-He213 Jian, Zhiqiang aut Huang, Yuhao aut Chen, Yu aut Zhou, Zhuoli aut Zheng, Nanning aut Enthalten in Science in China Heidelberg : Springer, 2001 62(2019), 8 vom: 12. Juli (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:62 year:2019 number:8 day:12 month:07 https://dx.doi.org/10.1007/s11432-018-9850-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 62 2019 8 12 07
allfields_unstemmed	10.1007/s11432-018-9850-9 doi (DE-627)SPR019330308 (SPR)s11432-018-9850-9-e DE-627 ger DE-627 rakwb eng Chen, Shitao verfasserin aut Autonomous driving: cognitive construction and situation understanding 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. autonomous driving (dpeaa)DE-He213 event-driven mechanism (dpeaa)DE-He213 cognitive construction (dpeaa)DE-He213 situation understanding (dpeaa)DE-He213 intuitive reasoning (dpeaa)DE-He213 Jian, Zhiqiang aut Huang, Yuhao aut Chen, Yu aut Zhou, Zhuoli aut Zheng, Nanning aut Enthalten in Science in China Heidelberg : Springer, 2001 62(2019), 8 vom: 12. Juli (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:62 year:2019 number:8 day:12 month:07 https://dx.doi.org/10.1007/s11432-018-9850-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 62 2019 8 12 07
allfieldsGer	10.1007/s11432-018-9850-9 doi (DE-627)SPR019330308 (SPR)s11432-018-9850-9-e DE-627 ger DE-627 rakwb eng Chen, Shitao verfasserin aut Autonomous driving: cognitive construction and situation understanding 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. autonomous driving (dpeaa)DE-He213 event-driven mechanism (dpeaa)DE-He213 cognitive construction (dpeaa)DE-He213 situation understanding (dpeaa)DE-He213 intuitive reasoning (dpeaa)DE-He213 Jian, Zhiqiang aut Huang, Yuhao aut Chen, Yu aut Zhou, Zhuoli aut Zheng, Nanning aut Enthalten in Science in China Heidelberg : Springer, 2001 62(2019), 8 vom: 12. Juli (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:62 year:2019 number:8 day:12 month:07 https://dx.doi.org/10.1007/s11432-018-9850-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 62 2019 8 12 07
allfieldsSound	10.1007/s11432-018-9850-9 doi (DE-627)SPR019330308 (SPR)s11432-018-9850-9-e DE-627 ger DE-627 rakwb eng Chen, Shitao verfasserin aut Autonomous driving: cognitive construction and situation understanding 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. autonomous driving (dpeaa)DE-He213 event-driven mechanism (dpeaa)DE-He213 cognitive construction (dpeaa)DE-He213 situation understanding (dpeaa)DE-He213 intuitive reasoning (dpeaa)DE-He213 Jian, Zhiqiang aut Huang, Yuhao aut Chen, Yu aut Zhou, Zhuoli aut Zheng, Nanning aut Enthalten in Science in China Heidelberg : Springer, 2001 62(2019), 8 vom: 12. Juli (DE-627)385614764 (DE-600)2142898-0 1862-2836 nnns volume:62 year:2019 number:8 day:12 month:07 https://dx.doi.org/10.1007/s11432-018-9850-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 62 2019 8 12 07
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source	Enthalten in Science in China 62(2019), 8 vom: 12. Juli volume:62 year:2019 number:8 day:12 month:07
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spellingShingle	Chen, Shitao misc autonomous driving misc event-driven mechanism misc cognitive construction misc situation understanding misc intuitive reasoning Autonomous driving: cognitive construction and situation understanding
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title_auth	Autonomous driving: cognitive construction and situation understanding
abstract	Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstractGer	Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstract_unstemmed	Abstract Autonomous vehicle is a kind of typical complex artificial intelligence system. In current research of autonomous driving, the most widely adopted technique is to use a basic framework of serial information processing and computations, which consists of four modules: perception, planning, decision-making, and control. However, this framework based on data-driven computing performs low computational efficiency, poor environmental understanding and self-learning ability. A neglected problem has long been how to understand and process environmental perception data from the sensors referring to the cognitive psychology level of the human driving process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is supposed to possess the mechanism of memorizing, inferring and experiential updating, enabling it to cope with traffic scenarios with high noise, dynamic, and randomness. In addition, for the process of understanding traffic scenes, the efficiency of event-related mechanism is more significant than single-attribute scenario perception data. Therefore, an effective self-driving method should not be confined to the traditional computing framework of ‘perception, planning, decision-making, and control’. It is necessary to explore a basic computing framework that conforms to human driver’s attention, reasoning, learning, and decision-making mechanism with regard to traffic scenarios and build an autonomous system inspired by biological intelligence. In this article, we review the basic methods and main progress in current data-driven autonomous driving technologies, deeply analyze the limitations and major problems faced by related algorithms. Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. The computing framework of autonomous driving based on a selective attention mechanism and intuitive reasoning discussed in this study can adapt to a more complex, open, and dynamic traffic environment. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019
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title_short	Autonomous driving: cognitive construction and situation understanding
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Then, combined with authors’ research, this study discusses how to implement a basic cognitive computing framework of self-driving with selective attention and an event-driven mechanism from the basic viewpoint of cognitive science. It further describes how to use multi-sensor and graph data with semantic information (such as traffic maps and a spatial correlation of events) to realize the associative representations of objects and drivable areas, as well as the intuitive reasoning method applied to understanding the situations in different traffic scenarios. 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Autonomous driving: cognitive construction and situation understanding

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