Name
Abstract | ||
|---|---|---|
This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events were acquired from a large-scale naturalistic driving data set. The kinematic variables from the image-based gap analysis were filtered by the weighted linear least squares in order to extrapolate them at the lane change time. In addition, the time-to-collision and required deceleration were computed, and potential safety threshold values are provided. The resulting range and range rate distributions showed directional discrepancies, i.e., in left lane changes, large trucks are often slower than other vehicles in the target lane, whereas they are usually faster in right lane changes. Video observations have confirmed that major motivations for changing lanes are different depending on the direction of move, i.e., moving to the left (faster) lane occurs due to a slower vehicle ahead or a merging vehicle on the right-hand side, whereas right lane changes are frequently made to return to the original lane after passing. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TITS.2015.2482821 | IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council |
Keywords | Field | DocType |
Active safety,gap analysis,lane change,large truck safety,naturalistic driving data | Truck,Computer vision,Radar imaging,Kinematics,Simulation,Image based,Acceleration,Artificial intelligence,Engineering,Mathematical model,Linear least squares,Active safety | Journal |
Volume | Issue | ISSN |
17 | 3 | IEEE Transactions on Intelligent Transportation Systems ( Volume:
17, Issue: 3, March 2016 ) |
Citations | PageRank | References |
2 | 0.38 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazutoshi Nobukawa | 1 | 22 | 2.08 |
| Shan Bao | 2 | 30 | 5.90 |
| David J. LeBlanc | 3 | 68 | 8.83 |
| Ding Zhao | 4 | 110 | 27.07 |
| Huei Peng | 5 | 805 | 150.82 |
| Christopher S. Pan | 6 | 25 | 4.24 |