Related papers: Shareable Driving Style Learning and Analysis with…
We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share…
Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; the probability of a link between any two nodes…
Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data…
Advancements in technology are steering attention toward creating comfortable and acceptable driving characteristics in autonomous vehicles. Ensuring a safe and comfortable ride experience is vital for the widespread adoption of autonomous…
Heavy vehicles (HVs) pose a significant challenge to maintaining a smooth traffic flow on the freeway because they are slower moving and create large blind spots. It is therefore desirable for the followers of HVs to perform lane changes…
Network autocorrelation models are widely used to evaluate the impact of social influence on some variable of interest. This is a large class of models that parsimoniously accounts for how one's neighbors influence one's own behaviors or…
Stream media content caching is a key enabling technology to promote the value chain of future urban vehicular networks. Nevertheless, the high mobility of vehicles, intermittency of information transmissions, high dynamics of user…
Driving behavior modeling is of great importance for designing safe, smart, and personalized autonomous driving systems. In this paper, an internal reward function-based driving model that emulates the human's decision-making mechanism is…
Predictions of driver's intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the…
High-level driving behavior decision-making is an open-challenging problem for connected vehicle technology, especially in heterogeneous traffic scenarios. In this paper, a deep reinforcement learning based high-level driving behavior…
Automated vehicles can change the society by improved safety, mobility and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to…
Following a leading vehicle is a daily but challenging task because it requires adapting to various traffic conditions and the leading vehicle's behaviors. However, the question `Does the following vehicle always actively react to the…
Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both…
Dynamic multilayer networks frequently represent the structure of multiple co-evolving relations; however, statistical models are not well-developed for this prevalent network type. Here, we propose a new latent space model for dynamic…
Shared mobility redefines urban transportation, offering economic and environmental benefits by reducing pollution and urban congestion. However, in the post-pandemic era, the shared mobility sector is grappling with a crisis of trust,…
A quantitative understanding of dynamic lane-changing (LC) interaction patterns is indispensable for improving the decision-making of autonomous vehicles, especially in mixed traffic with human-driven vehicles. This paper develops a novel…
With the development of autonomous driving, it is becoming increasingly common for autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel on the same roads. Existing single-vehicle planning algorithms on board struggle to…
This paper proposes a framework to recognize driving intentions and to predict driving behaviors of lane changing on the highway by using externally sensable traffic data from the host-vehicle. The framework consists of a driving…
This paper proposes a method for modeling human driver interactions that relies on multi-output gaussian processes. The proposed method is developed as a refinement of the game theoretical hierarchical reasoning approach called "level-k…
We investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy…