8 citations · 14 across the 5 of their papers we have counts for
7 papers
Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving
Junhao Ge, Zuhong Liu, Longteng Fan +5
End-to-end (E2E) autonomous driving (AD) models require diverse, high-quality data to perform well across various driving scenarios. However, collecting large-scale real-world data…
TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding
Zhejun Zhang, Christos Sakaridis, Luc Van Gool
In this technical report we present TrafficBots V1.5, a baseline method for the closed-loop simulation of traffic agents. TrafficBots V1.5 achieves baseline-level performance and a…
RESenv: A Realistic Earthquake Simulation Environment based on Unreal Engine
Yitong Sun, Hanchun Wang, Zhejun Zhang +2
Earthquakes have a significant impact on societies and economies, driving the need for effective search and rescue strategies. With the growing role of AI and robotics in these ope…
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
Zhejun Zhang, Alexander Liniger, Christos Sakaridis +2
The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future…
A Multiplicative Value Function for Safe and Efficient Reinforcement Learning
Nick Bührer, Zhejun Zhang, Alexander Liniger +2
An emerging field of sequential decision problems is safe Reinforcement Learning (RL), where the objective is to maximize the reward while obeying safety constraints. Being able to…
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
Zhejun Zhang, Alexander Liniger, Dengxin Dai +2
Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also be…
End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
Zhejun Zhang, Alexander Liniger, Dengxin Dai +2
End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that deman…