Search Results

In the dense fabric of urban areas, electric scooters have rapidly become a preferred mode of transportation. As they cater to modern mobility demands, they present significant safety challenges, especially when interacting with pedestrians. In general, e-scooters are suggested to be ridden in bike lanes/sidewalks or share the road with cars at the maximum speed of about 15-20 mph, which is more flexible and much faster than pedestrians and bicyclists. Accurate prediction of pedestrian movement, coupled with assistant motion control of scooters, is essential in minimizing collision risks and seamlessly integrating scooters in areas dense with pedestrians. Addressing these safety concerns, our research introduces a novel e-Scooter collision avoidance system (eCAS) with a method for predicting pedestrian trajectories, employing an advanced Long short-term memory (LSTM) network integrated with a state refinement module.

Roadway departure mitigation systems for helping to avoid and/or mitigate roadway departure collisions have been introduced by several vehicle manufactures in recent years. To support the development and performance evaluation of the roadway departure mitigation systems, a set of commonly seen roadside surrogate objects need to be developed. These objects include grass, curbs, metal guardrail, concrete divider, and traffic barrel/cones. This paper describes how to determine the representative color of these roadside surrogates. 24,762 locations with Google street view images were selected for the color determination of roadside objects. To mitigate the effect of the brightness to the color determination, the images not in good weather, not in bright daylight and under shade were manually eliminated. Then, the RGB values of the roadside objects in the remaining images were extracted.

Road departure is a leading cause of fatal crashes in the US and half of all the crashes are related to road departure [1]. Road departure warning (RDW) and road keeping assistance (RKA) are the new active safety areas to be explored. Most of the currently available road-departure detection technologies rely on the detection of lane markings, which are either missing or unclear in many roads. Therefore, in additional to the these lane markings, next-generation road departure detection should rely on the detection of other road edge and boundary objects. Common road edge and boundary indicators include lane marking, grass, curb, metal guardrail, concrete divider, traffic barrels and cones. This paper investigates the distribution of major types of road edges and road boundaries in the United States in order to enhance and evaluate the capabilities and effectiveness of RDW and RKA.