Assessment of AEB algorithms and relevance of datasets used for AEB assessment
| dc.contributor.author | Olleja, Pierluigi | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper | sv |
| dc.contributor.examiner | Bärgman, Jonas | |
| dc.contributor.supervisor | Lübbe, Nils | |
| dc.date.accessioned | 2019-10-18T09:55:17Z | |
| dc.date.available | 2019-10-18T09:55:17Z | |
| dc.date.issued | 2019 | sv |
| dc.date.submitted | 2019 | |
| dc.description.abstract | Road fatalities are a major concern in modern society. The increase in the amount of road vehicles on public roads exposes more people to the dangers that come with road traffic. On highways, higher speeds are the cause of more serious crashes. To tackle this issue, active safety systems work in cooperation with passive safety systems to improve the occupants’ safety. In the assessment of new active safety systems, the availability of crashes for computer simulation is limited. This work is therefore aimed first at using lead-vehicle-braking events from the highD naturalistic driving dataset as a basis for creating “what-if” crashes. These crashes were compared with real crashes from an in-depth crash database (GIDAS) and an AEB algorithm was assessed on both the datasets. The assessment was done using a common simulation framework, developed in this work, and the results of crash avoidance and mitigation were compared. Both datasets were collected on German highways. The results showed comparable trends for the relative velocities involved in crashes from both datasets, when comparing crashes where there was no reaction by the driver of the following vehicle. The AEB assessment didn’t show clear similarities in crash avoidance and mitigation. | sv |
| dc.identifier.coursecode | MMSX30 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/300480 | |
| dc.language.iso | eng | sv |
| dc.relation.ispartofseries | 2019:83 | sv |
| dc.setspec.uppsok | Technology | |
| dc.subject | Automated Emergency Braking (AEB) | sv |
| dc.subject | naturalistic driving dataset | sv |
| dc.subject | counterfactual simulations | sv |
| dc.subject | rear-end collision | sv |
| dc.subject | crash avoidance | sv |
| dc.title | Assessment of AEB algorithms and relevance of datasets used for AEB assessment | sv |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.uppsok | H | |
| local.programme | Automotive engineering (MPAUT), MSc |