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Daniel Goehring

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14 papers

1 author row

IROS Conference 2024 Conference Paper

Improving Out-of-Distribution Generalization of Trajectory Prediction for Autonomous Driving via Polynomial Representations

Yue Yao
Shengchao Yan
Daniel Goehring
Wolfram Burgard
Joerg Reichardt

Robustness against Out-of-Distribution (OoD) samples is a key performance indicator of a trajectory prediction model. However, the development and ranking of state-of-the-art (SotA) models are driven by their In-Distribution (ID) performance on individual competition datasets. We present an OoD testing protocol that homogenizes datasets and prediction tasks across two large-scale motion datasets. We introduce a novel prediction algorithm based on polynomial representations for agent trajectory and road geometry on both the input and output sides of the model. With a much smaller model size, training effort, and inference time, we reach near SotA performance for ID testing and significantly improve robustness in OoD testing. Within our OoD testing protocol, we further study two augmentation strategies of SotA models and their effects on model generalization. Highlighting the contrast between ID and OoD performance, we suggest adding OoD testing to the evaluation criteria of trajectory prediction models.

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ICRA Conference 2024 Conference Paper

Learning-Aided Warmstart of Model Predictive Control in Uncertain Fast-Changing Traffic

Mohamed-Khalil Bouzidi
Yue Yao
Daniel Goehring
Joerg Reichardt

Model Predictive Control lacks the ability to escape local minima in nonconvex problems. Furthermore, in fast-changing, uncertain environments, the conventional warmstart, using the optimal trajectory from the last timestep, often falls short of providing an adequately close initial guess for the current optimal trajectory. This can potentially result in convergence failures and safety issues. Therefore, this paper proposes a framework for learning-aided warmstarts of Model Predictive Control algorithms. Our method leverages a neural network based multimodal predictor to generate multiple trajectory proposals for the autonomous vehicle, which are further refined by a sampling-based technique. This combined approach enables us to identify multiple distinct local minima and provide an improved initial guess. We validate our approach with Monte Carlo simulations of traffic scenarios.

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IROS Conference 2024 Conference Paper

Multi-modal NeRF Self-Supervision for LiDAR Semantic Segmentation

Xavier Timoneda
Markus Herb
Fabian Duerr
Daniel Goehring
Fisher Yu 0001

LiDAR Semantic Segmentation is a fundamental task in autonomous driving perception consisting of associating each LiDAR point to a semantic label. Fully-supervised models have widely tackled this task, but they require labels for each scan, which either limits their domain or requires impractical amounts of expensive annotations. Camera images, which are generally recorded alongside LiDAR pointclouds, can be processed by the widely available 2D foundation models, which are generic and dataset-agnostic. However, distilling knowledge from 2D data to improve LiDAR perception raises domain adaptation challenges. For example, the classical perspective projection suffers from the parallax effect produced by the position shift between both sensors at their respective capture times. We propose a Semi-Supervised Learning setup to leverage unlabeled LiDAR pointclouds alongside distilled knowledge from the camera images. To self-supervise our model on the unlabeled scans, we add an auxiliary NeRF head and cast rays from the camera viewpoint over the unlabeled voxel features. The NeRF head predicts densities and semantic logits at each sampled ray location which are used for rendering pixel semantics. Concurrently, we query the Segment-Anything (SAM) foundation model with the camera image to generate a set of unlabeled generic masks. We fuse the masks with the rendered pixel semantics from LiDAR to produce pseudo-labels that supervise the pixel predictions. During inference, we drop the NeRF head and run our model with only LiDAR. We show the effectiveness of our approach in three public LiDAR Semantic Segmentation benchmarks: nuScenes, SemanticKITTI and ScribbleKITTI.

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IROS Conference 2023 Conference Paper

Cooperative LiDAR Localization and Mapping for V2X Connected Autonomous Vehicles

Bingyi Cao
Claas-Norman Ritter
Khaled Alomari
Daniel Goehring

Cooperative Simultaneous Localization and Mapping (C-SLAM) is an active research topic in mobile robotics. However, its application in the field of autonomous driving is rare. While the advent of Vehicle-to-Everything (V2X) communication has empowered Connected Autonomous Vehicles (CAV) to exchange data with each other, recent research on CAV cooperation tasks has primarily focused on cooperative perception and global positioning improvement. Techniques for organizing multiple CAV to work together to achieve localization and mapping in unknown environments have not been actively explored. We propose a C-SLAM system for CAVs that employs sparse LiDAR feature representations to enable vehicles to exchange data using standard V2X messages. The system was tested in real environments using two connected vehicles. The results show that the proposed V2X-based C-SLAM system can operate in both centralized and decentralized manners and output accurate pose estimates and global maps, showing promising application possibilities.

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IROS Conference 2021 Conference Paper

LiDAR-Based Object-Level SLAM for Autonomous Vehicles

Bingyi Cao
Ricardo Carrillo Mendoza
Andreas Philipp
Daniel Goehring

Simultaneous localization and mapping (SLAM) is an essential technique for autonomous driving. Recently, combining image recognition technology to generate semantically meaningful maps has become a new trend in visual SLAM research. However, in the field of LiDAR SLAM, this potential has not been fully explored. We propose a novel object-level SLAM system using 3D LiDARs for autonomous vehicles. We detect and track poles, walls, and parked cars, which are common along urban roads. This paper presents how we process the measurement data of three different shapes of objects to build a graph-based optimization system and facilitate the geometric distribution of poles to detect loops. Experiments were carried out on datasets collected with a test vehicle in city traffic. The results show that our object-level SLAM system can build precise and semantically meaningful maps and produce more accurate pose estimations compared to the state-of-the-art systems on our datasets.

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ICRA Conference 2019 Conference Paper

Analytic Collision Risk Calculation for Autonomous Vehicle Navigation

Andreas Philipp
Daniel Goehring

Collision checking and avoidance is an import part of the perception and planning system for autonomous driving. We present a new analytic approach to calculate the probability of a future collision and extend another already known solution to be suitable for ground vehicle navigation. Our new concept of the collision octagon facilitates in both cases the derivation of an analytic solution. Both approaches are compared to each other using simulated and real world scenarios. By comparing the results of the analytic solutions to the corresponding Monte Carlo simulations, their accuracy and real-time capability is demonstrated. The suitability of the analytic solutions for real world autonomous systems is further proven by integrating them into the trajectory prediction and planning system of the self-driving car of the Freie Universität Berlin.

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IROS Conference 2016 Conference Paper

Pole-based localization for autonomous vehicles in urban scenarios

Robert Spangenberg
Daniel Goehring
Raúl Rojas 0001

Localization is a key capability for autonomous vehicles especially in urban scenarios. We propose the use of pole-like landmarks as primary features in these environments, as they are distinct, long-term stable and can be detected reliably with a stereo camera system. Furthermore, the resulting map representation is memory efficient, allowing for easy storage and on-line updates. The localization is performed in real-time by a stereo camera system as a main sensor, using vehicle odometry and an off-the-shelf GPS as secondary information sources. Localization is performed by a particle filter approach, coupled with an Kalman filter for robustness and sensor fusion. This leads to a lateral accuracy below 20 cm in various urban test areas. The system has been included in our autonomous test vehicle and successfully demonstrated the full loop from mapping to autonomous driving.

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ICRA Conference 2016 Conference Paper

Traffic awareness driver assistance based on stereovision, eye-tracking, and head-up display

Tobias Langner 0002
Daniel Seifert
Bennet Fischer
Daniel Goehring
Tinosch Ganjineh
Raúl Rojas 0001

This paper presents a system which constantly monitors the level of attention of a driver in traffic. The vehicle is instrumented and can identify the state of traffic-lights, as well as obstacles on the road. If the driver is inattentive and fails to recognize a threat, the assistance system produces a warning. Therefore, the system helps the driver to focus on crucial traffic situations. Our system consists of three components: computer vision detection of traffic-lights and other traffic participants, an eye tracking device used also for head localization, and finally, a human machine interface consisting of a head-up display and an acoustic module used to provide warnings to the driver. The orientation of the driver's head is detected using fiducial markers visible in video frames. We describe how the system was integrated using an autonomous car as experimental ADAS platform.

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ICRA Conference 2014 Conference Paper

Interactive adaptation of real-time object detectors

Daniel Goehring
Judy Hoffman
Erik Rodner
Kate Saenko
Trevor Darrell

In the following paper, we present a framework for quickly training 2D object detectors for robotic perception. Our method can be used by robotics practitioners to quickly (under 30 seconds per object) build a large-scale real-time perception system. In particular, we show how to create new detectors on the fly using large-scale internet image databases, thus allowing a user to choose among thousands of available categories to build a detection system suitable for the particular robotic application. Furthermore, we show how to adapt these models to the current environment with just a few in-situ images. Experiments on existing 2D benchmarks evaluate the speed, accuracy, and flexibility of our system.

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IROS Conference 2013 Conference Paper

Grounding spatial relations for human-robot interaction

Sergio Guadarrama
Lorenzo Riano
Dave Golland
Daniel Goehring
Yangqing Jia
Dan Klein 0001
Pieter Abbeel
Trevor Darrell

We propose a system for human-robot interaction that learns both models for spatial prepositions and for object recognition. Our system grounds the meaning of an input sentence in terms of visual percepts coming from the robot's sensors in order to send an appropriate command to the PR2 or respond to spatial queries. To perform this grounding, the system recognizes the objects in the scene, determines which spatial relations hold between those objects, and semantically parses the input sentence. The proposed system uses the visual and spatial information in conjunction with the semantic parse to interpret statements that refer to objects (nouns), their spatial relationships (prepositions), and to execute commands (actions). The semantic parse is inherently compositional, allowing the robot to understand complex commands that refer to multiple objects and relations such as: “Move the cup close to the robot to the area in front of the plate and behind the tea box”. Our system correctly parses 94% of the 210 online test sentences, correctly interprets 91% of the correctly parsed sentences, and correctly executes 89% of the correctly interpreted sentences.

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ICRA Conference 2009 Conference Paper

Constraint based world modeling in mobile robotics

Daniel Goehring
Heinrich Mellmann
Hans-Dieter Burkhard

In this paper we present a novel approach using constraint based techniques for world modeling, i. e. self localization and object modeling. Within the last years, we have seen a reduction of landmarks such as beacons or colored goals within the RoboCup domain. Using other features as line information becomes more important. Using such sensor data is tricky, especially when the resulting position belief is stretched over a larger area. Constraints can overcome this limitations, as they have several advantages: they can represent large distributions and are easy to store and to communicate to other robots. Propagation of several constraints can be computationally cheap. Even high dimensional belief functions can be used. We will describe a sample implementation and show experimental results.

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ICRA Conference 2006 Conference Paper

Further Studies on the Use of Negative Information in Mobile Robot Localization

Jan Hoffmann 0001
Michael Spranger
Daniel Goehring
Matthias Jüngel
Hans-Dieter Burkhard

This paper deals with how the absence of an expected sensor reading can be used to improve Markov localization. Negative information has not been used for robot localization for various reasons like sensor imperfections, and occlusions that make it hard to determine if a missing sensor reading is really caused by the absence of a feature. We address these difficulties by carefully modeling the robot's main sensor, its camera. Taking into account the viewing frustum and detected obstacles, the absence of a sensor reading can be associated with the absence of that particular feature. This information can then be integrated into the localization process. We show the positive effect on robot localization in various experiments. (a) In a specific setup, the robot is able to localize using negative information where without it, it is unable to localize. (b) We demonstrate the importance of modeling occlusions and the impact of false negatives on localization. (c) We show the positive impact in a typical run

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IROS Conference 2006 Conference Paper

Multi Robot Object Tracking and Self Localization Using Visual Percept Relations

Daniel Goehring
Hans-Dieter Burkhard

In this paper we present a novel approach to estimating the position of objects tracked by a team of mobile robots and to use these objects for a better self localization. Modeling of moving objects is commonly done in a robo-centric coordinate frame because this information is sufficient for most low level robot control and it is independent of the quality of the current robot localization. For multiple robots to cooperate and share information, though, they need to agree on a global, allocentric frame of reference. When transforming the egocentric object model into a global one, it inherits the localization error of the robot in addition to the error associated with the egocentric model. We propose using the relation of objects detected in camera images to other objects in the same camera image as a basis for estimating the position of the object in a global coordinate system. The spatial relation of objects with respect to stationary objects (e. g. , landmarks) offers several advantages: a) Errors in feature detection are correlated and not assumed independent. Furthermore, the error of relative positions of objects within a single camera frame is comparably small, b) The information is independent of robot localization and odometry. c) As a consequence of the above, it provides a highly efficient method for communicating information about a tracked object and communication can be asynchronous, d) As the modeled object is independent from robo-centric coordinates, its position can be used for self localization of the observing robot. We present experimental evidence that shows how two robots are able to infer the position of an object within a global frame of reference, even though they are not localized themselves and then use this object information for self- localization

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IROS Conference 2005 Conference Paper

Making use of what you don't see: negative information in Markov localization

Jan Hoffmann 0001
Michael Spranger
Daniel Goehring
Matthias Jüngel

This paper explores how the absence of an expected sensor reading can be used to improve Markov localization. This negative information usually is not being used in localization, because it yields less information than positive information (i. e. sensing a landmark), and a sensor often fails to detect a landmark, even if it falls within its sensing range. We address these difficulties by carefully modeling the sensor to avoid false negatives. This can also be thought of as adding an additional sensor that detects the absence of an expected landmark. We show how such modeling is done and how it is integrated into Markov localization. In real world experiments, we demonstrate that a robot is able to localize in positions where otherwise it could not and quantify our findings using the entropy of the particle distribution. Exploiting negative information leads to a greatly improved localization performance and reactivity.

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