The Impact of Pedestrian Crossing Flags on Driver Yielding Behavior in Las Vegas, NV

Walking is the most affordable, accessible, and environmentally friendly method of transportation. However, the risk of pedestrian injury or death from motor vehicle crashes is significant, particularly in sprawling metropolitan areas. The purpose of this study was to examine the effect of pedestrian crossing flags (PCFs) on driver yielding behaviors. Participants crossed a marked, midblock crosswalk on a multilane road in Las Vegas, Nevada, with and without PCFs, to determine if there were differences in driver yielding behaviors (n = 160 crossings). Trained observers recorded (1) the number of vehicles that passed in the nearest lane without yielding while the pedestrian waited at the curb and (2) the number of vehicles that passed through the crosswalk while the pedestrian was in the same half of the roadway. ANOVA revealed that drivers were significantly less likely to pass through the crosswalk with the pedestrian in the roadway when they were carrying a PCF (M = 0.20; M = 0.06); drivers were more likely to yield to the pedestrian waiting to enter the roadway when they were carrying a PCF (M = 1.38; M = 0.95). Pedestrian crossing flags are a low-tech, low-cost intervention that may improve pedestrian safety at marked mid-block crosswalks. Future research should examine driver fade-out effects and more advanced pedestrian safety alternatives

Spatiotemporal Stacked Sequential Learning for Pedestrian Detection

Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera.Comment: 8 pages, 5 figure, 1 tabl