| 1. |
- Chen, Shuangshuang, 1992-, et al.
(författare)
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Amortized Variational Inference for Road Friction Estimation
- 2020
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Ingår i: 2020 IEEE Intelligent Vehicles Symposium (IV). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1777-1784, s. 1777-1784
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Konferensbidrag (refereegranskat)abstract
- Road friction estimation concerns inference of the coefficient between the tire and road surface to facilitate active safety features. Current state-of-the-art methods lack generalization capability to cope with different tire characteristics and models are restricted when using Bayesian inference in estimation while recent supervised learning methods lack uncertainty prediction on estimates. This paper introduces variational inference to approximate intractable posterior of friction estimates and learns an amortized variational inference model from tire measurement data to facilitate probabilistic estimation while sustaining the flexibility of tire models. As a by-product, a probabilistic tire model can be learned jointly with friction estimator model. Experiments on simulated and field test data show that the learned friction estimator provides accurate estimates with robust uncertainty measures in a wide range of tire excitation levels. Meanwhile, the learned tire model reflects well-studied tire characteristics from field test data.
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| 2. |
- Muppirisetty, L. Srikar, et al.
(författare)
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Location-aided pilot contamination elimination for massive MIMO systems
- 2016
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Ingår i: 2015 IEEE Global Communications Conference, GLOBECOM 2015. - New York : IEEE conference proceedings.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Massive MIMO systems, while being a promising technology for 5G systems, face a number of practical challenges. Among those, pilot contamination stands out as a key bottleneck to design high-capacity beamforming methods. We propose and analyze a location-aided approach to reduce the pilot contamination effect in uplink channel estimation for massive MIMO systems. The proposed method exploits the location of user terminals, scatterers, and base stations. The approach removes the need for direct estimation of large covariance matrices and provides good channel estimation performance in the large antenna regime.
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| 3. |
- Piccoli, Francesco, et al.
(författare)
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FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
- 2020
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Ingår i: Conference Record - Asilomar Conference on Signals, Systems and Computers. - 1058-6393. ; 2020-November, s. 68-72
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Konferensbidrag (refereegranskat)abstract
- Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving. In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios. Our framework relies on objection detection bounding boxes combined with skeletal features of human pose. We study early, late, and combined (early and late) fusion mechanisms to exploit the skeletal features and reduce false positives as well to improve the intention prediction performance. The early fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for pedestrian intention classification. Furthermore, we propose three new metrics to properly evaluate the pedestrian intention systems. Under these new evaluation metrics for the intention prediction, the proposed end-to-end network offers accurate pedestrian intention up to half a second ahead of the actual risky maneuver.
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| 4. |
- Yilmaz, Kaan, et al.
(författare)
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AV-SLAM: Autonomous vehicle SLAM with gravity direction initialization
- 2020
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. ; , s. 8093-8100
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Konferensbidrag (refereegranskat)abstract
- Simultaneous localization and mapping (SLAM) algorithms aimed for autonomous vehicles (AVs) are required to utilize sensor redundancies specific to AVs and enable accurate, fast and repeatable estimations of pose and path trajectories. In this work, we present a combination of three SLAM algorithms that utilize a different subset of available sensors such as inertial measurement unit (IMU), a gray-scale mono-camera, and a Lidar. Also, we propose a novel acceleration-based gravity direction initialization (AGI) method for the visual-inertial SLAM algorithm. We analyze the SLAM algorithms and initialization methods for pose estimation accuracy, speed of convergence and repeatability on the KITTI odometry sequences. The proposed VI-SLAM with AGI method achieves relative pose errors less than 2%, convergence in half a minute or less and convergence time variability less than 3s, which makes it preferable for AVs.
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