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- Title
- EXAMINING METHODS FOR IDENTIFYING THE OCCURRENCE OF SECONDARY CRASHES
- Creator
- Nouri, Hadis
- Date
- 2022
- Collection
- Electronic Theses & Dissertations
- Description
-
ABSTRACTTraffic crashes are a particular concern in urban areas, where the occurrence of a collision heightens the risk of subsequent secondary crashes upstream, particularly under high levels of traffic congestion. There is considerable difficulty in estimating the number of such crashes, and in identifying roadway locations and circumstances where the risks of such crashes are most pronounced. In light of these concerns, there is significant value in advancing our understanding of these...
Show moreABSTRACTTraffic crashes are a particular concern in urban areas, where the occurrence of a collision heightens the risk of subsequent secondary crashes upstream, particularly under high levels of traffic congestion. There is considerable difficulty in estimating the number of such crashes, and in identifying roadway locations and circumstances where the risks of such crashes are most pronounced. In light of these concerns, there is significant value in advancing our understanding of these issues, including our ability to predict and mitigate the potential for secondary crashes on freeways. A significant challenge in this regard is the ability to effectively identify a secondary crash with respect to the both the spatial temporal thresholds within which secondary crashes occur. Contemporary approaches are often based on static spatiotemporal impact windows, or on dynamic approaches that consider traffic flow conditions. Both methods are subject to important limitations that are investigated as a part of this research. As a part of this study, crash data from the Michigan interstate system was used to identify secondary crashes. A detailed review of police crash reports is conducted to verify which crashes are secondary in nature by examining standard fields on the report form, as well as information from the narrative section completed by the investigating officer. The influence of spatiotemporal window sizing (relative to the time and location of the primary crash) is explored with respect to the sensitivity and specificity of secondary crash detection in order to determine thresholds that yield minimal error. A static approach based on a large number of predefined window sizes was used to compare the rate of secondary crash identification. The static method was shown to consistently overestimate secondary crash occurrence and these results varied across thresholds sizes. Subsequent efforts used a dynamic approach, where the window size was varied based upon changes in speed profiles on the associated road segments. Real-time traffic and speed data were used to identify secondary crashes and the results vary considerably based upon the method employed. The research also identified contextual environments where the risks of secondary crashes are most pronounced through the estimation of a series of regression models, culminating in guidance to assist road agencies in effectively monitoring and clearing crashes and other incidents to minimize the potential for secondary crashes.
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- Title
- 3D Object Detection and Tracking for Autonomous Vehicles
- Creator
- Pang, Su
- Date
- 2022
- Collection
- Electronic Theses & Dissertations
- Description
-
Autonomous driving systems require accurate 3D object detection and tracking to achieve reliable path planning and navigation. For object detection, there have been significant advances in neural networks for single-modality approaches. However, it has been surprisingly difficult to train networks to use multiple modalities in a way that demonstrates gain over single-modality networks. In this dissertation, we first propose three networks for Camera-LiDAR and Camera-Radar fusion. For Camera...
Show moreAutonomous driving systems require accurate 3D object detection and tracking to achieve reliable path planning and navigation. For object detection, there have been significant advances in neural networks for single-modality approaches. However, it has been surprisingly difficult to train networks to use multiple modalities in a way that demonstrates gain over single-modality networks. In this dissertation, we first propose three networks for Camera-LiDAR and Camera-Radar fusion. For Camera-LiDAR fusion, CLOCs (Camera-LiDAR Object Candidates fusion) and Fast-CLOCs are presented. CLOCs fusion provides a multi-modal fusion framework that significantly improves the performance of single-modality detectors. CLOCs operates on the combined output candidates before Non-Maximum Suppression (NMS) of any 2D and any 3D detector, and is trained to leverage their geometric and semantic consistencies to produce more accurate 3D detection results. Fast-CLOCs can run in near real-time with less computational requirements compared to CLOCs. Fast-CLOCs eliminates the separate heavy 2D detector, and instead uses a 3D detector-cued 2D image detector (3D-Q-2D) to reduce memory and computation. For Camera-Radar fusion, we propose TransCAR, a Transformer-based Camera-And-Radar fusion solution for 3D object detection. The cross-attention layer within the transformer decoder can adaptively learn the soft-association between the radar features and vision queries instead of hard-association based on sensor calibration only. Then, we propose to solve the 3D multiple object tracking (MOT) problem for autonomous driving applications using a random finite set-based (RFS) Multiple Measurement Models filter (RFS-M3). In particular, we propose multiple measurement models for a Poisson multi-Bernoulli mixture (PMBM) filter in support of different application scenarios. Our RFS-M3 filter can naturally model these uncertainties accurately and elegantly. We combine learning-based detections with our RFS-M3 tracker by incorporating the detection confidence score into the PMBM prediction and update step. We have evaluated our CLOCs, Fast-CLOCs and TransCAR fusion-based 3D detector and RFS-M3 3D tracker using challenging datasets including KITTI, nuScenes, Argoverse and Waymo that are released by academia and industry leaders. Superior experimental results demonstrated the effectiveness of the proposed approaches.
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