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Found 9 papers, 2 papers with code
Learning Realistic Traffic Agents in Closed-loop
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.
MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation
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.
GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting
However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent.
Deep Parametric Continuous Convolutional Neural Networks
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)
End-to-end Interpretable Neural Motion Planner
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.
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
We show TrafficSim generates significantly more realistic and diverse traffic scenarios as compared to a diverse set of baselines.
StrObe: Streaming Object Detection from LiDAR Packets
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.
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants.
The Importance of Prior Knowledge in Precise Multimodal Prediction
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.
