Abstract
In first embodiments of automated driving systems the customer acceptance will depend on well-defined cooperation between the driver and the automated vehicle. Design rules for supervised driving automation have to be identified and confirmed. The capability to establish such guidelines depends on understanding and analysis of quantitative and qualitative traffic factors in a real environment. The selected automation system under review is called Traffic Jam Assistance (TJA). Based on data of a naturalistic driving study in traffic congestions in Europe the investigation builds on the method of video data coding and its statistical analysis. Analysis of video and sensor data revealed potential indicators for relevant traffic manoeuvres such as cut-in or cut-out. These indicators serve to support the future design of a warning taxonomy defining when the driver either needs to regain situation awareness or a transition from automated to manual driving has to be initiated.
| Original language | English |
|---|---|
| Publication status | Published - 1 Jan 2014 |
| Event | 35th FISITA World Automotive Congress 2014: Intelligent transport to solve our future mobility, safety and environmental challenges - Maastricht, Netherlands Duration: 2 Jun 2014 → 6 Jun 2014 Conference number: 35 |
Conference
| Conference | 35th FISITA World Automotive Congress 2014 |
|---|---|
| Country | Netherlands |
| City | Maastricht |
| Period | 2/06/14 → 6/06/14 |
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Keywords
- Lane change analysis
- Naturalistic driving study
- Traffic jam assist
- Transition taxonomy
- Video labelling
Cite this
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Analysis of traffic congestions for automated driving with cooperative approach. / Urhahne, Joseph A.; Steiger, Reid; van der Voort, Mascha C.
2014. Paper presented at 35th FISITA World Automotive Congress 2014, Maastricht, Netherlands.Research output: Contribution to conference › Paper › Academic › peer-review
TY - CONF
T1 - Analysis of traffic congestions for automated driving with cooperative approach
AU - Urhahne, Joseph A.
AU - Steiger, Reid
AU - van der Voort, Mascha C.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In first embodiments of automated driving systems the customer acceptance will depend on well-defined cooperation between the driver and the automated vehicle. Design rules for supervised driving automation have to be identified and confirmed. The capability to establish such guidelines depends on understanding and analysis of quantitative and qualitative traffic factors in a real environment. The selected automation system under review is called Traffic Jam Assistance (TJA). Based on data of a naturalistic driving study in traffic congestions in Europe the investigation builds on the method of video data coding and its statistical analysis. Analysis of video and sensor data revealed potential indicators for relevant traffic manoeuvres such as cut-in or cut-out. These indicators serve to support the future design of a warning taxonomy defining when the driver either needs to regain situation awareness or a transition from automated to manual driving has to be initiated.
AB - In first embodiments of automated driving systems the customer acceptance will depend on well-defined cooperation between the driver and the automated vehicle. Design rules for supervised driving automation have to be identified and confirmed. The capability to establish such guidelines depends on understanding and analysis of quantitative and qualitative traffic factors in a real environment. The selected automation system under review is called Traffic Jam Assistance (TJA). Based on data of a naturalistic driving study in traffic congestions in Europe the investigation builds on the method of video data coding and its statistical analysis. Analysis of video and sensor data revealed potential indicators for relevant traffic manoeuvres such as cut-in or cut-out. These indicators serve to support the future design of a warning taxonomy defining when the driver either needs to regain situation awareness or a transition from automated to manual driving has to be initiated.
KW - Lane change analysis
KW - Naturalistic driving study
KW - Traffic jam assist
KW - Transition taxonomy
KW - Video labelling
UR - http://www.scopus.com/inward/record.url?scp=85060470332&partnerID=8YFLogxK
M3 - Paper
ER -