Multisensor simultaneous vehicle tracking and shape estimation

Elfring, Jos; Appeldoorn, Rein; Kwakkernaat, Maurice

doi:10.1109/ivs.2016.7535453

Cited by 8 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several of such approaches have been proposed for radar-based tracking. These include clustering and extraction of reference points as in [1], [2], or fitting bounding boxes and L-shapes [3], [4], reflection center models [5], or velocity profiles [6]- [8] to the data. While preprocessing routines are oftentimes effective, computationally fast, and lead to clearly separable system architectures, they face difficulties if the data from a single time step is ambiguous and the correct meta-measurement cannot be easily extracted.…”

Section: Introductionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

High-resolution radar sensors are able to resolve multiple detections per object and therefore provide valuable information for vehicle environment perception. For instance, multiple detections allow to infer the size of an object or to more precisely measure the object's motion. Yet, the increased amount of data raises the demands on tracking modules: measurement models that are able to process multiple detections for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for tracking vehicles using radar detections and demonstrates how this model can be incorporated into a Random-Finite-Setbased multi-object filter. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multiobject tracker, the entire process chain from the raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data and it is demonstrated that the data-driven measurement model outperforms a manually designed model.

show abstract

Section: Introductionmentioning

confidence: 99%

Tracking Multiple Vehicles Using a Variational Radar Model

Scheel

Dietmayer

2019

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…This module takes as input a predicted position of the vehicle’s center and calculates the minimum length of the vehicle in case the side radar detects the presence of this vehicle. More details about this application and technical details can be found in [32]. None of the architectures in Section 2 support using this sensor set due to interface requirements, and therefore, it is not possible to reproduce these results without changing the architecture of these works.…”

Section: Applications and Resultsmentioning

confidence: 99%

“…This is why a Rao-Blackwellized particle filter was selected. More technical details can be found in [32]. The resulting world model while driving on a highway is shown in Figure 9.…”

Section: Applications and Resultsmentioning

confidence: 99%

“…Furthermore, feeding lower level data to the state estimation algorithm requires more advanced measurement models and a thorough understanding of the low-level sensor data. Low-level sensor data fusion is used in, among others, [31,32,33]. …”

Section: Related Workmentioning

confidence: 99%

“…The Bayesian MHT filter framework for example includes probabilities for target birth and track termination [55], and joint integrated probabilistic data association adds the target existence concept to the JPDAF. Alternatives include [60], where existence probabilities are used for track management and more heuristics-based approaches where tracks are initiated or terminated based on, e.g., (the absence of) consecutive updates [32]. …”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

Elfring

Appeldoorn

Dries

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

The number of perception sensors on automated vehicles increases due to the increasing number of advanced driver assistance system functions and their increasing complexity. Furthermore, fail-safe systems require redundancy, thereby increasing the number of sensors even further. A one-size-fits-all multisensor data fusion architecture is not realistic due to the enormous diversity in vehicles, sensors and applications. As an alternative, this work presents a methodology that can be used to effectively come up with an implementation to build a consistent model of a vehicle’s surroundings. The methodology is accompanied by a software architecture. This combination minimizes the effort required to update the multisensor data fusion system whenever sensors or applications are added or replaced. A series of real-world experiments involving different sensors and algorithms demonstrates the methodology and the software architecture.

show abstract

An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking

Gosala

Meng²

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

Radar sensors have become an important part of the perception sensor suite due to their long range and their ability to work in adverse weather conditions. However, several shortcomings such as large amounts of noise and extreme sparsity of the point cloud result in them not being used to their full potential. In this paper, we present a novel Recursive Least Squares (RLS) based approach to estimate the instantaneous velocity of dynamic objects in real-time that is capable of handling large amounts of noise in the input data stream. We also present an end-to-end pipeline to track extended objects in real-time that uses the computed velocity estimates for data association and track initialisation. The approaches are evaluated using several real-world inspired driving scenarios that test the limits of these algorithms. It is also experimentally proven that our approaches run in real-time with frame execution time not exceeding 30 ms even in dense traffic scenarios, thus allowing for their direct implementation on autonomous vehicles.

show abstract

Multisensor simultaneous vehicle tracking and shape estimation

Cited by 8 publications

References 14 publications

Tracking Multiple Vehicles Using a Variational Radar Model

Tracking Multiple Vehicles Using a Variational Radar Model

Effective World Modeling: Multisensor Data Fusion Methodology for Automated Driving

An RLS-Based Instantaneous Velocity Estimator for Extended Radar Tracking

Contact Info

Product

Resources

About