no code implementations • 6 Dec 2023 • Anshul Nayak, Azim Eskandarian
Occlusion-aware planning often requires communicating the information of the occluded object to the ego agent for safe navigation.
no code implementations • 15 Sep 2023 • Ning Ding, Azim Eskandarian
Object detection is a crucial component of autonomous driving, and many detection applications have been developed to address this task.
no code implementations • 26 May 2023 • Anshul Nayak, Azim Eskandarian, Zachary Doerzaph, Prasenjit Ghorai
For the current research, we consider an encoder-decoder based deep ensemble network for capturing both perception and predictive uncertainty simultaneously.
1 code implementation • 11 May 2023 • Ning Ding, Ce Zhang, Azim Eskandarian
On the other hand, unknown objects, which have not been seen in training sample set, are one of the reasons that hinder autonomous vehicles from driving beyond the operational domain.
no code implementations • 7 Nov 2022 • Ning Ding, Ce Zhang, Azim Eskandarian
A lack of driver's vigilance is the main cause of most vehicle crashes.
no code implementations • 4 May 2022 • Anshul Nayak, Azim Eskandarian, Zachary Doerzaph
Past research on pedestrian trajectory forecasting mainly focused on deterministic predictions which provide only point estimates of future states.
no code implementations • 4 Mar 2022 • Ce Zhang, Azim Eskandarian
The results demonstrate that the proposed evaluation metric accurately assesses the detection quality of camera-based systems in autonomous driving environments.
no code implementations • 21 Jun 2021 • Ce Zhang, Azim Eskandarian, Xuelai Du
The output from the proposed algorithm is a surrounding environment complexity parameter.
no code implementations • 24 Jan 2021 • Ce Zhang, Young-Keun Kim, Azim Eskandarian
The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network, which showed to be highly efficient and accurate for time-series classification.
no code implementations • 25 Aug 2020 • Ce Zhang, Azim Eskandarian
Then, the EEG signal preprocessing, feature extraction, and classification algorithms for driver state detection are reviewed.
no code implementations • 25 Aug 2020 • Ce Zhang, Azim Eskandarian
The experiment results show the proposed algorithm average computation time is 37. 22% less than the FBCSP (1st winner in the BCI Competition IV) and 4. 98% longer than the conventional CSP method.
no code implementations • 24 Aug 2020 • Seontake Oh, Ji-Hwan You, Azim Eskandarian, Young-Keun Kim
A misalignment of LiDAR as low as a few degrees could cause a significant error in obstacle detection and mapping that could cause safety and quality issues.
no code implementations • 24 Aug 2020 • Ji-Hwan You, Seon Taek Oh, Jae-Eun Park, Azim Eskandarian, Young-Keun Kim
This paper presents a novel automatic calibration system to estimate the extrinsic parameters of LiDAR mounted on a mobile platform for sensor misalignment inspection in the large-scale production of highly automated vehicles.