Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos

To ensure the safe and efficient navigation of autonomous vehicles and advanced driving assistance systems in complex traffic scenarios, predicting the future bounding boxes of surrounding traffic agents is crucial. However, simultaneously predicting the future location and scale of target traffic agents from the egocentric view poses challenges due to the vehicle's egomotion causing considerable field-of-view changes. Moreover, in anomalous or risky situations, tracking loss or abrupt motion changes limit the available observation time, requiring learning of cues within a short time window. Existing methods typically use a simple concatenation operation to combine different cues, overlooking their dynamics over time. To address this, this paper introduces the Fusion-Gated Recurrent Unit (Fusion-GRU) network, a novel encoder-decoder architecture for future bounding box localization. Unlike traditional GRUs, Fusion-GRU accounts for mutual and complex interactions among input features. Moreover, an intermediary estimator coupled with a self-attention aggregation layer is also introduced to learn sequential dependencies for long range prediction. Finally, a GRU decoder is employed to predict the future bounding boxes. The proposed method is evaluated on two publicly available datasets, ROL and HEV-I. The experimental results showcase the promising performance of the Fusion-GRU, demonstrating its effectiveness in predicting future bounding boxes of traffic agents

PROMOTING CSET OUTREACH ACTIVITIES THROUGH SAFETY DATA MANAGEMENT AND ANALYSIS IN RITI COMMUNITIES

Traffic crashes are one of the leading causes of death among all people in the United States, but the rates among American Indian and Alaska Native (AIAN) populations are significantly higher than other groups. In fact, rural areas in general are disadvantaged from a traffic safety perspective due to the lack of funding and challenges in safety improvement decisions. This may contribute to the much higher fatality rate on rural roadways than on urban roadways. Additionally, there is a known issue of underreporting of fatal crashes of tribal members. Thus, an increased focus on rural, isolated, tribal, and indigenous (RITI) community traffic safety is necessary in order to progress towards zero fatalities. The need for quality data is recognized, and even included in many tribal transportation plans, but implementation and collection of the data varies. Quality data enables better safety analysis and enables greater support for traffic safety improvements. An easy-to-use and multisource database would enable tribes throughout the state and other rural communities to more readily manage data and apply for improvement funding. In order to reach this point, it is necessary to have agreements with tribes on crash data collection and usage, and understand local customs, needs, and current practices. This research aimed to form trusting and lasting relationships with tribal leaders in Washington State in order to facilitate crash database management and traffic safety analysis in their communities. The outreach activities included meetings with local tribal leaders, interviews, and attendance and presentations at tribal conferences. Ultimately a formal research agreement was signed with one tribe in Washington State granting access to the fatal and serious injury crash data they had collected