Interaction-aware Kalman Neural Networks for Trajectory Prediction

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex scenes always yield great challenges in modeling the patterns of surrounding traffic. For example, one main challenge comes from the intractable interaction effects in a complex traffic system. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter network. Attributed to the multiple traffic data sources, our end-to-end trainable approach technically fuses dynamic and interaction-aware trajectories boosting the prediction performance. Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium (IV) 202

Design of Driving Behavior Pattern Measurements Using Smartphone Global Positioning System Data

ABSTRACTThe emergence of new technologies such as GPS, cellphone, Bluetooth device, etc. offers opportunities for collecting high-fidelity temporal-spatial travel data in a cost-effective manner. With the vehicle trajectory data achieved from a smartphone app Metropia, this study targets on exploring the trajectory data and designing the measurements of the driving pattern. Metropia is a recently available mobile traffic app that uses prediction and coordinating technology combined with user rewards to incentivize drivers to cooperate, balance traffic load on the network, and reduce traffic congestion. Speed and celeration (acceleration and deceleration) are obtained from the Metropia platform directly and parameterized as individual and system measurements related to traffic, spatial and temporal conditions. A case study is provided in this paper to demonstrate the feasibility of this approach utilizing the trajectory data from the actual app usage. The driving behaviors at both individual and system levels are quantified from the microscopic speed and celeration records. The results from this study reveal distinct driving behavior pattern and shed lights for further opportunities to identify behavior characteristics beyond safety and environmental considerations

Exploring Consumer Value Path of Cross-Border E-Commerce: A Perspective of Means-End Theory

Despite the explosive growth of CBEC, research into the phenomenon has not increased proportionally. Prior studies mainly discuss the opportunities, challenges and critical elements in CBEC on the organizational level. Little research has explored the individual consumer’s psychological processes of joining and the benefits derived from purchasing on CBEC. Filling the research gap identified above, the objective of this study is to construct the hierarchical value map (HVM) of CBEC illustrating how consumers pursue their end through the decision making process consisting the linkage from perceived attributes to desired benefits and the eventual customer value when using CBEC. For this purpose, a qualitative rather than a quantitative approach ought to substantiate prior findings by uncovering the key defining components of CBEC context. The HVM presents 4 important benefits obtaining paths marked as boldfaced, referred as economic oriented path, efficacy oriented obtaining path, choice optimization path, and shipping progress oriented path