no code implementations • 2 Nov 2023 • Chris Zhang, James Tu, Lunjun Zhang, Kelvin Wong, Simon Suo, Raquel Urtasun
Our experiments show that RTR learns more realistic and generalizable traffic simulation policies, achieving significantly better tradeoffs between human-like driving and traffic compliance in both nominal and long-tail scenarios.
no code implementations • CVPR 2023 • Simon Suo, Kelvin Wong, Justin Xu, James Tu, Alexander Cui, Sergio Casas, Raquel Urtasun
Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations.
no code implementations • 4 Nov 2022 • Alexander Cui, Sergio Casas, Kelvin Wong, Simon Suo, Raquel Urtasun
However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent.
no code implementations • CVPR 2018 • Shenlong Wang, Simon Suo, Wei-Chiu Ma, Andrei Pokrovsky, Raquel Urtasun
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks.
Ranked #2 on Semantic Segmentation on S3DIS Area5 (Number of params metric)
1 code implementation • CVPR 2019 • Wenyuan Zeng, Wenjie Luo, Simon Suo, Abbas Sadat, Bin Yang, Sergio Casas, Raquel Urtasun
In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users.
1 code implementation • CVPR 2021 • Simon Suo, Sebastian Regalado, Sergio Casas, Raquel Urtasun
We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
no code implementations • 12 Nov 2020 • Davi Frossard, Simon Suo, Sergio Casas, James Tu, Rui Hu, Raquel Urtasun
In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
no code implementations • ECCV 2020 • Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants.
no code implementations • 4 Jun 2020 • Sergio Casas, Cole Gulino, Simon Suo, Raquel Urtasun
Towards this goal, we design a framework that leverages REINFORCE to incorporate non-differentiable priors over sample trajectories from a probabilistic model, thus optimizing the whole distribution.