Identification of Threat Regions From a Dynamic Occupancy Grid Map for Situation-Aware Environment Perception

The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between the computational complexity of algorithms and their potential to ensure safe operation of automated vehicles are often encountered. Situation-aware environment perception presents one promising example, where computational resources are distributed to regions within the perception area that are relevant for the task of the automated vehicle. While prior map knowledge is often leveraged to identify relevant regions, in this work, we present a lightweight identification of safety-relevant regions that relies solely on online information. We show that our approach enables safe vehicle operation in critical scenarios, while retaining the benefits of non-uniformly distributed resources within the environment perception.Comment: Accepted for publication at the 25th IEEE International Conference on Intelligent Transportation Systems 2022. V2: added IEEE copyright notice V3: Added DO

Fast Long-Term Multi-Scenario Prediction for Maneuver Planning at Unsignalized Intersections

Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the right of way, is often handled implicitly in the prediction. However, an infrastructure-based maneuver planning can assign artificial priorities between cooperative vehicles, so it needs to evaluate many more potential scenarios. Additionally, the prediction horizon has to be long enough to assess the impact of a maneuver. We, therefore, present a novel long-term prediction approach handling the gap acceptance estimation and the velocity prediction in two separate stages. Thereby, the behavior of regular vehicles as well as priority assignments of cooperative vehicles can be considered. We train both stages on real-world traffic observations to achieve realistic prediction results. Our method has a competitive accuracy and is fast enough to predict a multitude of scenarios in a short time, making it suitable to be used in a maneuver planning framework

Graph-based Trajectory Prediction with Cooperative Information

For automated driving, predicting the future trajectories of other road users in complex traffic situations is a hard problem. Modern neural networks use the past trajectories of traffic participants as well as map data to gather hints about the possible driver intention and likely maneuvers. With increasing connectivity between cars and other traffic actors, cooperative information is another source of data that can be used as inputs for trajectory prediction algorithms. Connected actors might transmit their intended path or even complete planned trajectories to other actors, which simplifies the prediction problem due to the imposed constraints. In this work, we outline the benefits of using this source of data for trajectory prediction and propose a graph-based neural network architecture that can leverage this additional data. We show that the network performance increases substantially if cooperative data is present. Also, our proposed training scheme improves the network's performance even for cases where no cooperative information is available. We also show that the network can deal with inaccurate cooperative data, which allows it to be used in real automated driving environments.Comment: Accepted for publication at the 26th IEEE International Conference on Intelligent Transportation Systems 202

Environment Modeling Based on Generic Infrastructure Sensor Interfaces Using a Centralized Labeled-Multi-Bernoulli Filter

Urban intersections put high demands on fully automated vehicles, in particular, if occlusion occurs. In order to resolve such and support vehicles in unclear situations, a popular approach is the utilization of additional information from infrastructure-based sensing systems. However, a widespread use of such systems is circumvented by their complexity and thus, high costs. Within this paper, a generic interface is proposed, which enables a huge variety of sensors to be connected. The sensors are only required to measure very few features of the objects, if multiple distributed sensors with different viewing directions are available. Furthermore, a Labeled Multi-Bernoulli (LMB) filter is presented, which can not only handle such measurements, but also infers missing object information about the objects' extents. The approach is evaluated on simulations and demonstrated on a real-world infrastructure setup

Automated Static Camera Calibration with Intelligent Vehicles

Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated manner, we present a robust extrinsic calibration method for automated geo-referenced camera calibration. Our method requires a calibration vehicle equipped with a combined GNSS/RTK receiver and an inertial measurement unit (IMU) for self-localization. In order to remove any requirements for the target's appearance and the local traffic conditions, we propose a novel approach using hypothesis filtering. Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle. Furthermore, we do not limit road access for other road users during calibration. We demonstrate the feasibility and accuracy of our approach by evaluating our approach on synthetic datasets as well as a real-world connected intersection, and deploying the calibration on real infrastructure. Our source code is publicly available.Comment: 7 pages, 3 figures, accepted for presentation at the 34th IEEE Intelligent Vehicles Symposium (IV 2023), June 4 - June 7, 2023, Anchorage, Alaska, United States of Americ

LACI: Low-effort Automatic Calibration of Infrastructure Sensors

Sensor calibration usually is a time consuming yet important task. While classical approaches are sensor-specific and often need calibration targets as well as a widely overlapping field of view (FOV), within this work, a cooperative intelligent vehicle is used as callibration target. The vehicleis detected in the sensor frame and then matched with the information received from the cooperative awareness messagessend by the coperative intelligent vehicle. The presented algorithm is fully automated as well as sensor-independent, relying only on a very common set of assumptions. Due to the direct registration on the world frame, no overlapping FOV is necessary. The algorithm is evaluated through experiment for four laserscanners as well as one pair of stereo cameras showing a repetition error within the measurement uncertainty of the sensors. A plausibility check rules out systematic errors that might not have been covered by evaluating the repetition error.Comment: 6 pages, published at ITSC 201

Kontextbasierte Bewegungsplanung automatisierter Fahrzeuge an Kreuzungen mit vernetzten Lichtsignalanlagen

Die Vielzahl an Situationen, welche im urbanen Straßenverkehr vorkommen, stellt aktuelle Bewegungsplanungsverfahren in automatisierten Fahrzeugen immer noch vor große Herausforderungen. Während die Modellierung zusätzlicher Szenarien für klassische Verfahren oft sehr mühsam ist, erleichtert die kontextbasierte Bewegungsplanung diese Erweiterung. Um dies zu demonstrieren, wird in diesem Beitrag ein bestehendes kontextbasiertes Verfahren um das Überqueren einer Kreuzung mit vernetzter Lichtsignalanlage erweitert. Diese stellt auch prädiktive Informationen zur Verfügung, welche durch die Bewegungsplanung im Fahrzeug gewinnbringend verwendet werden. Das vorgestellte Verfahren wird simulativ und im realen Verkehr evaluiert. Gegenüber einer rein reaktiven Planung zeigen sich dabei deutliche Vorteile

Video: Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

Motion planning at urban intersections that accounts for the situation context, handles occlusions and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. As classical methods subdivide the motion planning into decision making and trajectory planning, they either come with a narrowed solution set or finding a feasible trajectory is not guaranteed. In this work, motion planning is formulated as an optimal control problem (OCP) and holistically solved for exploring all available decision options. The OCP is parametrized and simplified according to the situation context extracted from map and perception information. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track (T2T) fusion, the information is used in parallel. The uncertainties are handled by a risk model that bridges the gap between set-based methods and probabilistic approaches. Particularly, for vanishing risk, the formal guarantees of set-based methods are inherited, while otherwise, the guarantees are softened to guarantees in a probabilistic sense. This video shows a short presentation of the motion planning approach as well as results from the real-world experiments

Deep Kernel Learning for Uncertainty Estimation in Multiple Trajectory Prediction Networks

Predicting future paths of vehicles or pedestrians is an essential task for automated vehicles to allow for planning the own trajectory. Using predicted paths, a planning algorithm can, e.g., react to anticipated manoeuvres of other traffic participants. For calculating risks of planned manoeuvres, it is essential that the predicted paths are generated with information about their uncertainty. Since today's state of the art trajectory prediction algorithms are based on deep neural networks (DNNs), the estimation of uncertainty is left to the neural networks as well, which usually provide no means of assessing how the uncertainty estimation works. In this paper, we present a combination of DNNs with Gaussian processes via Deep Kernel Learning (DKL), which combines the ability of DNNs to perform the prediction task with the advantage of Gaussian processes of having more interpretable probabilistic outputs. We propose and evaluate two different variants for the task of multimodal trajectory prediction using Stochastic Variational Gaussian Processes (SVGPs) and the recently proposed regression method Deep Sigma Point Processes (DSPPs), respectively. We evaluate the predictive distributions of both approaches on the publicly available Argoverse Motion Forecasting dataset and compare them to other, purely neural network based methods for uncertainty estimation

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address this challenge with a sampling-based optimization approach. For this, we formulate an optimal control problem that optimizes for low risk and high passenger comfort. The risk is calculated on the basis of the perception information and the respective uncertainty using a risk model. The risk model combines set-based methods and probabilistic approaches. Thus, the approach provides safety guarantees in a probabilistic sense, while for a vanishing risk, the formal safety guarantees of the set-based methods are inherited. By exploring all available behavior options, our approach solves decision making and longitudinal trajectory planning in one step. The available behavior options are provided by a formal representation of the situation context, which is also used to reduce calculation efforts. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track fusion, the information is used in parallel. The motion planning scheme is validated through real-world experiments