no code implementations • 22 Dec 2023 • Ayoub Raji, Nicola Musiu, Alessandro Toschi, Francesco Prignoli, Eugenio Mascaro, Pietro Musso, Francesco Amerotti, Alexander Liniger, Silvio Sorrentino, Marko Bertogna
In this paper, we present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar.
no code implementations • 9 Nov 2023 • Lei LI, Alexander Liniger, Mario Millhaeusler, Vagia Tsiminaki, Yuanyou Li, Dengxin Dai
In this paper, we develop a novel knowledge distillation approach to shrink the performance gap between these two modalities.
no code implementations • 27 Oct 2023 • Ayoub Raji, Danilo Caporale, Francesco Gatti, Andrea Giove, Micaela Verucchi, Davide Malatesta, Nicola Musiu, Alessandro Toschi, Silviu Roberto Popitanu, Fabio Bagni, Massimiliano Bosi, Alexander Liniger, Marko Bertogna, Daniele Morra, Francesco Amerotti, Luca Bartoli, Federico Martello, Riccardo Porta
The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars.
no code implementations • 20 Oct 2023 • Andrea Boscolo Camiletto, Alfredo Bochicchio, Alexander Liniger, Dengxin Dai, Abel Gawel
Efficient relocalization is essential for intelligent vehicles when GPS reception is insufficient or sensor-based localization fails.
2 code implementations • NeurIPS 2023 • Zhejun Zhang, Alexander Liniger, Christos Sakaridis, Fisher Yu, Luc van Gool
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 trajectories of surrounding traffic participants.
no code implementations • 25 Jul 2023 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
Thus, online estimation of the lane graph is crucial for widespread and reliable autonomous navigation.
no code implementations • ICCV 2023 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection outputs.
no code implementations • 3 Apr 2023 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
One of the most common and useful representation of such an understanding is done in the form of BEV lane graphs.
3 code implementations • 7 Mar 2023 • Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, Luc van Gool
We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving, and based on TrafficBots we obtain a world model tailored for the planning module of autonomous vehicles.
1 code implementation • 7 Mar 2023 • Nick Bührer, Zhejun Zhang, Alexander Liniger, Fisher Yu, Luc van Gool
To this end, we propose a safe model-free RL algorithm with a novel multiplicative value function consisting of a safety critic and a reward critic.
no code implementations • 14 Nov 2022 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
On the one hand, the proposed method learns to segment these planar hulls from the labeled data.
no code implementations • 17 Sep 2022 • Soomin Lee, Le Chen, Jiahao Wang, Alexander Liniger, Suryansh Kumar, Fisher Yu
In this paper, we tackle the problem of active robotic 3D reconstruction of an object.
no code implementations • 22 Jul 2022 • Ayoub Raji, Alexander Liniger, Andrea Giove, Alessandro Toschi, Nicola Musiu, Daniele Morra, Micaela Verucchi, Danilo Caporale, Marko Bertogna
This paper presents a multi-layer motion planning and control architecture for autonomous racing, capable of avoiding static obstacles, performing active overtakes, and reaching velocities above 75 $m/s$.
1 code implementation • 25 May 2022 • Ge-Peng Ji, Deng-Ping Fan, Yu-Cheng Chou, Dengxin Dai, Alexander Liniger, Luc van Gool
This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD).
1 code implementation • CVPR 2022 • Vaishakh Patil, Christos Sakaridis, Alexander Liniger, Luc van Gool
We focus on the supervised setup, in which ground-truth depth is available only at training time.
Ranked #6 on
Depth Estimation
on NYU-Depth V2
no code implementations • 5 Apr 2022 • Jose L. Vazquez, Alexander Liniger, Wilko Schwarting, Daniela Rus, Luc van Gool
Fundamental to the success of our method is the design of a novel multi-agent policy network that can steer a vehicle given the state of the surrounding agents and the map information.
no code implementations • CVPR 2022 • Jan-Nico Zaech, Alexander Liniger, Martin Danelljan, Dengxin Dai, Luc van Gool
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
no code implementations • 19 Dec 2021 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
We use a Transformer-based architecture to detect the keypoints, as well as to summarize the visual context of the image.
1 code implementation • CVPR 2022 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points.
2 code implementations • ICCV 2021 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
Ranked #3 on
Lane Detection
on nuScenes
2 code implementations • ICCV 2021 • Zhejun Zhang, Alexander Liniger, Dengxin Dai, Fisher Yu, Luc van Gool
Our end-to-end agent achieves a 78% success rate while generalizing to a new town and new weather on the NoCrash-dense benchmark and state-of-the-art performance on the challenging public routes of the CARLA LeaderBoard.
no code implementations • 12 Aug 2021 • Edoardo Mello Rella, Jan-Nico Zaech, Alexander Liniger, Luc van Gool
Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems.
no code implementations • 23 Apr 2021 • Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Martin Danelljan, Luc van Gool
Tracking of objects in 3D is a fundamental task in computer vision that finds use in a wide range of applications such as autonomous driving, robotics or augmented reality.
1 code implementation • 7 Mar 2021 • Anton Obukhov, Maxim Rakhuba, Alexander Liniger, Zhiwu Huang, Stamatios Georgoulis, Dengxin Dai, Luc van Gool
We study low-rank parameterizations of weight matrices with embedded spectral properties in the Deep Learning context.
1 code implementation • 5 Dec 2020 • Yigit Baran Can, Alexander Liniger, Ozan Unal, Danda Paudel, Luc van Gool
In this work, we study scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera.
no code implementations • 26 Nov 2020 • Eugenio Chisari, Alexander Liniger, Alisa Rupenyan, Luc van Gool, John Lygeros
We present a reinforcement learning-based solution to autonomously race on a miniature race car platform.
no code implementations • 10 Jul 2020 • Simon Hecker, Dengxin Dai, Alexander Liniger, Luc van Gool
This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like.
4 code implementations • 18 Jun 2020 • Manish Prajapat, Kamyar Azizzadenesheli, Alexander Liniger, Yisong Yue, Anima Anandkumar
A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties.
1 code implementation • 15 May 2020 • Alexander Liniger, Luc van Gool
This formulation allows us to compute safe sets using tools from viability theory, that can be used as terminal constraints in an optimization-based motion planner.
Robotics Systems and Control Systems and Control Optimization and Control
no code implementations • 29 Apr 2020 • Jan-Nico Zaech, Dengxin Dai, Alexander Liniger, Luc van Gool
Our second contribution lies in applying the method to the well-known traffic agent tracking and prediction dataset Argoverse, resulting in 228, 000 action sequences.
no code implementations • 3 Apr 2020 • Martin Hahner, Dengxin Dai, Alexander Liniger, Luc van Gool
In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection.
no code implementations • 12 Jul 2019 • Simon Hecker, Alexander Liniger, Henrik Maurenbrecher, Dengxin Dai, Luc van Gool
Our contributes are fourfold: 1) we predict the motorcycle's intra-lane position using a convolutional neural network (CNN), 2) we predict the motorcycle roll angle using a CNN, 3) we use an upgraded controller model that incorporates road incline for a more realistic model and prediction, 4) we design a scale-able system by utilizing HERE Technologies map database to obtain the accurate road geometry of the future path.
4 code implementations • 13 May 2019 • Juraj Kabzan, Miguel de la Iglesia Valls, Victor Reijgwart, Hubertus Franciscus Cornelis Hendrikx, Claas Ehmke, Manish Prajapat, Andreas Bühler, Nikhil Gosala, Mehak Gupta, Ramya Sivanesan, Ankit Dhall, Eugenio Chisari, Napat Karnchanachari, Sonja Brits, Manuel Dangel, Inkyu Sa, Renaud Dubé, Abel Gawel, Mark Pfeiffer, Alexander Liniger, John Lygeros, Roland Siegwart
This paper presents the algorithms and system architecture of an autonomous racecar.
Robotics
5 code implementations • 20 Nov 2017 • Alexander Liniger, Alexander Domahidi, Manfred Morari
This paper describes autonomous racing of RC race cars based on mathematical optimization.
Optimization and Control Robotics Systems and Control
no code implementations • 17 Nov 2017 • Lukas Hewing, Alexander Liniger, Melanie N. Zeilinger
This paper presents an adaptive high performance control method for autonomous miniature race cars.