In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

We propose a motion forecasting model that exploits a novel structured map representation
as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we …

High-definition maps (HD maps) are a key component of most modern self-driving systems
due to their valuable semantic and geometric information. Unfortunately, building HD maps …

Simulation has the potential to massively scale evaluation of self-driving systems, enabling
rapid development as well as safe deployment. Bridging the gap between simulation and …

In this paper we propose a novel end-to-end learnable network that performs joint
perception, prediction and motion planning for self-driving vehicles and produces …

Trajectory prediction is a critical functionality of autonomous systems that share
environments with uncontrolled agents, one prominent example being self-driving vehicles …

The task of motion forecasting is critical for self-driving vehicles (SDV s) to be able to plan a
safe maneuver. Towards this goal, modern approaches reason about the map, the agents' …

Autonomous vehicle (AV) stacks are typically built in a modular fashion, with explicit
components performing detection, tracking, prediction, planning, control, etc. While …

In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which
performs object detection, motion prediction, and motion planning with a single neural …

Trajectory prediction is a safety-critical tool for autonomous vehicles to plan and execute
actions. Our work addresses two key challenges in trajectory prediction, learning multimodal …