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20172026
most citedOptimization-Based Autonomous Racing of 1:43 Scale RC Cars

549 citations · 1.5k across the 32 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CV2023★ 8 cited

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…

cs.CV2023★ 11 cited

U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization

Andrea Boscolo Camiletto, Alfredo Bochicchio, Alexander Liniger +2

Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails. Recent advances in Bird's-Eye-View (BEV) segme…

cs.CV2023

Prior Based Online Lane Graph Extraction from Single Onboard Camera Image

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1

The local road network information is essential for autonomous navigation. This information is commonly obtained from offline HD-Maps in terms of lane graphs. However, the local ro…

cs.CV2023

Improving Online Lane Graph Extraction by Object-Lane Clustering

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1

Autonomous driving requires accurate local scene understanding information. To this end, autonomous agents deploy object detection and online BEV lane graph extraction methods as a…

cs.CV2023

Online Lane Graph Extraction from Onboard Video

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1

Autonomous driving requires a structured understanding of the surrounding road network to navigate. One of the most common and useful representation of such an understanding is don…

cs.RO2023

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…