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Lack of shock absorption capability of conventional steel bollards causes significant vehicle damage and, consequently, high repair costs. This research studies a solution to reduce vehicle damage by inserting polylactic acid (PLA) honeycomb structures. A honeycomb-inserted bollard was designed based on numerical simulations using LS-DYNA, which yielded the bollard designed for actual vehicle-bollard collision experiments. Simulation efforts were focused on calculating the acceleration characteristics when a vehicle collides with steel and honeycomb-inserted bollards. Compared to the simulated steel bollards, 20 MPa yield-strength honeycomb-inserted bollard showed 0.017 s delay in the maximum acceleration occurrence time, reduction of the maximum acceleration of 37.4% of that of steel bollards, and a 13.1% reduction in the B-pillar maximum acceleration. Actual vehicle-bollard collision experiments, with a gyro-sensor installed at the test vehicle front bumper frame, also proved improved shock absorption characteristics of the honeycomb-inserted bollards. An experiment with honeycomb-inserted bollard showed a 0.783 s delay in the maximum acceleration occurrence time, a significant delay when compared to steel bollards. The maximum acceleration measured by the gyro-sensor was 0.35 &times, 103 m/s2 when the simulation predicted it to be 0.388 &times, 103 m/s2, proving the similarity in the simulations and experiments. Thus, this study of shock absorption characteristics promised reduced damage to vehicles and lower repair cost.

The blind encounter commuting risks, such as failing to recognize and avoid obstacles while walking, but protective support systems are lacking. Acoustic signals at crosswalk lights are activated by button or remote control, however, these signals are difficult to operate and not always available (i.e., broken). Bollards are posts installed for pedestrian safety, but they can create dangerous situations in that the blind cannot see them. Therefore, we proposed an obstacle recognition system to assist the blind in walking safely outdoors, this system can recognize and guide the blind through two obstacles (crosswalk lights and bollards) with image training from the Google Object Detection application program interface (API) based on TensorFlow. The recognized results notify the blind through voice guidance playback in real time. The single shot multibox detector (SSD) MobileNet and faster region-convolutional neural network (R-CNN) models were applied to evaluate the obstacle recognition system, the latter model demonstrated better performance. Crosswalk lights were evaluated and found to perform better during the day than night. They were also analyzed to determine if a client could cross at a crosswalk, while the locations of bollards were analyzed by algorithms to guide the client by voice guidance.