We generalize the derivation of model predictive path integral control (MPPI) to allow for a single joint distribution across controls in the control sequence.
This paper presents a method for characterizing failures of LiDAR-based perception systems for AVs in adverse weather conditions.
We propose a novel framework for real-time communication-aware coverage control in networked robot swarms.
We evaluate our method on a variety of generative models, including variational autoencoders and auto-regressive architectures.
While the rules are governed by interpretable parameters of the driver model, these parameters are learned online from driving demonstration data using particle filtering.
A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode.
Discrete latent spaces in variational autoencoders have been shown to effectively capture the data distribution for many real-world problems such as natural language understanding, human intent prediction, and visual scene representation.
We can use driving data collected over a long period of time to extract rich information about how vehicles behave in different areas of the roads.
Further, with the use of high-fidelity driving simulators and real-world datasets, we demonstrate how parameters of 2D and 3D occupancy maps can be automatically adapted to accord with local spatial changes.
This paper describes Burn-InfoGAIL, which allows for disentanglement of latent variability in demonstrations.
In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories.
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels.
We consider the problem of building continuous occupancy representations in dynamic environments for robotics applications.