6 accepted papers
Modern high-definition LIDAR is expensive for commercial autonomous driving vehicles and small indoor robots. An affordable solution to this problem is fusion of planar LIDAR with RGB images to provide a similar level of perception capability. Even though state-of-the-art methods provide approaches…
Interactive behavior is important for autonomous driving vehicles, especially for scenarios like ramp merging which require significant social interaction between autonomous driving vehicles and human-driven cars. This paper enhances our previous Probabilistic Graphical Model (PGM) merging control m…
Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on-road for lane changes are rarely considered. In…
Interactive driving is challenging but essential for autonomous cars in dense traffic or urban areas. Proper interaction requires understanding and prediction of future trajectories of all neighboring cars around a target vehicle. Current solutions typically assume a certain distribution or stochast…
Nowadays, self-driving cars are being applied to more complex urban scenarios including intersections, merging ramps or lane changes. It is, therefore, important for self-driving cars to behave socially with human-driven cars. In this paper, we focus on generating the lane change behavior for self-d…
This paper investigates real-time on-road motion planning algorithms for autonomous passenger vehicles (APV) in urban environments, and propose a computationally efficient planning formulation. Two key properties, tunability and stability, are emphasized when designing the proposed planner. The main…