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Inertial Detection of Unusual Driving Events for Self-driving
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wang, Hairong Gruteser, Marco |
| Copyright Year | 2019 |
| Abstract | OF THE THESIS INERTIAL DETECTION OF UNUSUAL DRIVING EVENTS FOR SELF-DRIVING by HAIRONG WANG Thesis Director: Marco Gruteser While modern self-driving vehicles have shown their impressive capabilities towards offering new mobility to millions of people, it still remains challenging to build fully dependable and safe self-driving systems. To ensure the dependability of automated driving, the self-driving system is not only required to understand common road situations, but also widely different unusual events (e.g., objects on the roadway, pedestrian crossing highway, deer standing next to the road, etc.), which are very rare but more likely to cause unanticipated accidents. To detect unusual events, existing approaches seek to collect them by driving millions of miles with self-driving prototypes. But there still remains uncertainty because of limited miles covered. In contrast, this thesis proposes automatic unusual driving events identification algorithms, which can detect unusual cases through inertial sensing from in-vehicle devices in human-driven vehicles. This approach can be scaled to a much larger number of vehicles and thereby cover |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://rucore.libraries.rutgers.edu/rutgers-lib/60073/PDF/1/play/ |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
