549 citations · 1.5k across the 32 of their papers we have counts for
11 papers · 1 filter
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…
Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds
Ayoub Raji, Alexander Liniger, Andrea Giove +6
This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching ve…
Deep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models
Jose L. Vazquez, Alexander Liniger, Wilko Schwarting +2
In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are separated, limiting the planner to react to predictions that are not informed by the planne…
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
Johannes Betz, Hongrui Zheng, Alexander Liniger +5
The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware…
Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction
Edoardo Mello Rella, Jan-Nico Zaech, Alexander Liniger +1
Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems. It is a challenging problem as agents a…
A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver
Sirish Srinivasan, Sebastian Nicolas Giles, Alexander Liniger
We present a holistically designed three layer control architecture capable of outperforming a professional driver racing the same car. Our approach focuses on the co-design of the…
Learning from Simulation, Racing in Reality
Eugenio Chisari, Alexander Liniger, Alisa Rupenyan +2
We present a reinforcement learning-based solution to autonomously race on a miniature race car platform. We show that a policy that is trained purely in simulation using a relativ…
Safe Motion Planning for Autonomous Driving using an Adversarial Road Model
Alexander Liniger, Luc van Gool
This paper presents a game-theoretic path-following formulation where the opponent is an adversary road model. This formulation allows us to compute safe sets using tools from viab…
Optimization-Based Hierarchical Motion Planning for Autonomous Racing
José L. Vázquez, Marius Brühlmeier, Alexander Liniger +2
In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-le…
AMZ Driverless: The Full Autonomous Racing System
Juraj Kabzan, Miguel de la Iglesia Valls, Victor Reijgwart +19
This paper presents the algorithms and system architecture of an autonomous racecar. The introduced vehicle is powered by a software stack designed for robustness, reliability, and…