Name
Abstract | ||
|---|---|---|
Vision-based active safety and driver assistance systems, such as lane departure warning (LDW) system, have increasingly gained popularity in automotive applications due to their fairly good performance at relatively low cost. Lane detection is a very complicated and time-consuming process and the potential time delay resulting from current image capturing and image processing can potentially impact the performance of the system. The LDW system as discussed in this report may consequently produce a value of time-to-lane-crossing (TLC), which can lead to false alarm or warning at inappropriate time to driver. This paper presents a theoretical method that dynamically compensates the time delay, and thus, enhances the potential the TLC calculation can reduce false alarms. Simulation results show that under the test parameters, the proposed theoretical method can be more effective in computing a TLC by compensating for the excessive time delay. The approach will be further evaluated and tested in the continued work with consideration of a higher-fidelity vehicle dynamics model and a variety of lane models. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ITSC.2012.6338653 | ITSC |
Keywords | Field | DocType |
image capturing,road safety,image processing,lane detection,automobiles,vision-based active safety,automotive application,time-to-lane-crossing,computer vision,driver information systems,higher-fidelity vehicle dynamics model,lane departure warning system,vehicle dynamics,dynamic delay compensation,driver assistance system,geometry,warning systems | Warning system,False alarm,Lane departure warning system,Simulation,Advanced driver assistance systems,Image processing,Vehicle dynamics,Engineering,Active safety,Automotive industry | Conference |
Volume | Issue | ISSN |
null | null | 2153-0009 E-ISBN : 978-1-4673-3062-6 |
ISBN | Citations | PageRank |
978-1-4673-3062-6 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sumin Zhang | 1 | 5 | 2.51 |
| Weiwen Deng | 2 | 54 | 17.16 |
| Ye Wang | 3 | 1 | 1.06 |
| Qingrong Zhao | 4 | 7 | 1.90 |
| Bakhtiar B. Litkouhi | 5 | 0 | 0.34 |