Author name cluster

Quinlan Sykora

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2 papers

1 author row

ICRA Conference 2024 Conference Paper

QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving

Sourav Biswas 0001
Sergio Casas 0002
Quinlan Sykora
Ben Agro
Abbas Sadat
Raquel Urtasun

A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. However, object detection assumes a discrete set of objects and loses information about uncertainty, so any errors compound when predicting the future behavior of those agents. Alternatively, dense occupancy grid maps have been utilized to understand free-space. However, predicting a grid for the entire scene is wasteful since only certain spatio-temporal regions are reachable and relevant to the self-driving vehicle. We present a unified, interpretable, and efficient autonomy framework that moves away from cascading modules that first perceive, then predict, and finally plan. Instead, we shift the paradigm to have the planner query occupancy at relevant spatio-temporal points, restricting the computation to those regions of interest. Exploiting this representation, we evaluate a candidate trajectory around key factors such as collision avoidance, comfort, and progress for safety and interpretability. Our approach achieves better highway driving quality than the state-of-the-art on high-fidelity closed-loop simulations.

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ICML Conference 2020 Conference Paper

Multi-Agent Routing Value Iteration Network

Quinlan Sykora
Mengye Ren
Raquel Urtasun

In this paper we tackle the problem of routing multiple agents in a coordinated manner. This is a complex problem that has a wide range of applications in fleet management to achieve a common goal, such as mapping from a swarm of robots and ride sharing. Traditional methods are typically not designed for realistic environments which contain sparsely connected graphs and unknown traffic, and are often too slow in runtime to be practical. In contrast, we propose a graph neural network based model that is able to perform multi-agent routing based on learned value iteration in a sparsely connected graph with dynamically changing traffic conditions. Moreover, our learned communication module enables the agents to coordinate online and adapt to changes more effectively. We created a simulated environment to mimic realistic mapping performed by autonomous vehicles with unknown minimum edge coverage and traffic conditions; our approach significantly outperforms traditional solvers both in terms of total cost and runtime. We also show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.

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