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 …

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 …

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 …

This paper proposes an online multi-camera multi-object tracker that only requires
monocular detector training, independent of the multi-camera configurations, allowing …

In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which
utilizes radar sensor information along with widely used lidar and high definition (HD) maps …

To navigate safely in urban environments, an autonomous vehicle (ego vehicle) must
understand and anticipate its surroundings, in particular the behavior and intents of other …

We present a novel method for testing the safety of self-driving vehicles in simulation. We
propose an alternative to sensor simulation, as sensor simulation is expensive and has …

Predicting the behavior of road users, particularly pedestrians, is vital for safe motion
planning in the context of autonomous driving systems. Traditionally, pedestrian behavior …

This paper presents a vulnerable road user (VRU) tracking algorithm capable of handling
noisy and missing detections from heterogeneous sensors. We propose a cooperative …

With the advancement in computer vision deep learning, systems now are able to analyze
an unprecedented amount of rich visual information from videos to enable applications such …