LiDAR Data Segmentation in Off-Road Environment Using Convolutional Neural Networks (CNN)

Dabbiru, Lalitha; Goodin, Chris; Scherrer, Nicklaus; Carruth, Daniel W.

doi:10.4271/2020-01-0696

Cited by 23 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An open-source software library for simulating the sensors, environment, and vehicle dynamics of autonomous ground vehicles (AGV), MAVS was created to provide real-time, physics-based simulation capability in a modular, customizable architecture that can be integrated with a variety of other systems and software like the Robotic Operating System (ROS). The MAVS uses ray-tracing to generate realistic sensor data for Light Detection and Ranging (lidar) sensors, cameras, and global-positioning system (GPS) [8]. It features a C++ API, as well as a Python interface to the API that allows for rapid development of simulated experiments.…”

Section: Synthetic Aerial Data Generationmentioning

confidence: 99%

Object detection in synthetic aerial imagery using deep learning

Dabbiru

Goodin

Carruth

et al. 2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

View full text Add to dashboard Cite

Object detection in aerial images is a challenging task as some objects are only a few pixels wide, some objects are occluded, and some are in shade. With the cost of drones decreasing, there is a surge in the amount of aerial data, so it will be useful if models can extract valuable features from the aerial data. Convolutional neural networks (CNN) are a useful tool for object detection and machine learning applications. However, machine learning requires labeled data to train and test the CNN models. In this work, we used a simulator to automatically generate labeled synthetic aerial imagery to use in the training and testing of machine learning algorithms. The synthetic aerial data used in this work was developed using a physics-based software tool called Mississippi State University Autonomous Vehicle Simulator (MAVS). We generated a dataset of 871 aerial images of 640x480 resolution and implemented Keras-RetinaNet framework with ResNet 50 as backbone for object detection. Keras-RetinaNet is one of the popular object detection models to be used with aerial imagery. As a preliminary task, we detected buildings in the synthetic aerial imagery and our results show a high mAP (mean Average Precision) accuracy of 77.99% using the state-of-the-art RetinaNet model.

show abstract

Section: Synthetic Aerial Data Generationmentioning

confidence: 99%

Object detection in synthetic aerial imagery using deep learning

Dabbiru

Goodin

Carruth

et al. 2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

View full text Add to dashboard Cite

show abstract

“…For example, in academia, developers from CMU and MIT used TROCS and Talos simulators, respectively, to test their algorithms in simulation before porting them to the vehicle for practical road test (74,75). Recently researchers have used simulated LiDAR data to develop and test algorithms for AV off-road ground navigation using the MSU autonomous vehicle simulator (76). To supply the critical events and corner cases for the evaluations of AVs efficiently and effectively, Feng et al (77) leveraged RL algorithms to generate naturalistic adversarial critical events in CARLA to test the safety performance of AVs.…”

Section: Simulator Datasetsmentioning

confidence: 99%

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

Zhou

Laval

Zhou

et al. 2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Self-driving technology companies and the research community are accelerating the pace of use of machine learning longitudinal motion planning (mMP) for autonomous vehicles (AVs). This paper reviews the current state of the art in mMP, with an exclusive focus on its impact on traffic congestion. The paper identifies the availability of congestion scenarios in current datasets, and summarizes the required features for training mMP. For learning methods, the major methods in both imitation learning and non-imitation learning are surveyed. The emerging technologies adopted by some leading AV companies, such as Tesla, Waymo, and Comma.ai, are also highlighted. It is found that: (i) the AV industry has been mostly focusing on the long tail problem related to safety and has overlooked the impact on traffic congestion, (ii) the current public self-driving datasets have not included enough congestion scenarios, and mostly lack the necessary input features/output labels to train mMP, and (iii) although the reinforcement learning approach can integrate congestion mitigation into the learning goal, the major mMP method adopted by industry is still behavior cloning, whose capability to learn a congestion-mitigating mMP remains to be seen. Based on the review, the study identifies the research gaps in current mMP development. Some suggestions for congestion mitigation for future mMP studies are proposed: (i) enrich data collection to facilitate the congestion learning, (ii) incorporate non-imitation learning methods to combine traffic efficiency into a safety-oriented technical route, and (iii) integrate domain knowledge from the traditional car-following theory to improve the string stability of mMP.

show abstract

“…Either from the point of research or from the point of its applicability in fields such as robotics, forest studies, military, etc., autonomous driving in off-road environment is gaining increasing attention nowadays. In off-road environments, semantic segmentation [1]- [4] is often used for understanding of scenes around the vehicle. Per-pixel segmentation assigns labels to each pixel in a frame of data according to detection and classification of objects in the scene.…”

Section: Introductionmentioning

confidence: 99%

CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving

et al. 2022

Self Cite

View full text Add to dashboard Cite

In the context of autonomous driving, the existing semantic segmentation concept strongly supports on-road driving where hard inter-class boundaries are enforced and objects can be categorized based on their visible structures with high confidence. Due to the well-structured nature of typical onroad scenarios, current road extraction processes are largely successful and most types of vehicles are able to traverse through the area that is detected as road. However, the off-road driving domain has many additional uncertainties such as uneven terrain structure, positive and negative obstacles, ditches, quagmires, hidden objects, etc. making it very unstructured. Traversing through such unstructured area is constrained by a vehicle's type and its capability. Therefore, an alternative approach to segmentation of the off-road driving trail is required that supports consideration of the vehicle type in a way that is not considered in state-of-the-art on-road segmentation approaches. To overcome this limitation and facilitate the path extraction in the off-road driving domain, we propose traversability concept and corresponding dataset which is based on the notion that the driving trails should be finely resolved into different sub-trails and areas corresponding to the capability of different vehicle classes in order to achieve safe traversal. Based on this, we consider three different classes of vehicles (sedan, pickup, and off-road) and label the images corresponding to the traversing capability of those vehicles. So the proposed dataset facilitates the segmentation of off-road driving trail into three regions based on the nature of the driving area and vehicle capability. We call this dataset as CaT (CAVS Traversability) dataset and is publicly available at https://www.cavs.msstate.edu/resources/downloads/CaT/CaT.tar.gz.

show abstract

LiDAR Data Segmentation in Off-Road Environment Using Convolutional Neural Networks (CNN)

Cited by 23 publications

References 13 publications

Object detection in synthetic aerial imagery using deep learning

Object detection in synthetic aerial imagery using deep learning

Review of Learning-Based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps Between Self-Driving and Traffic Congestion

CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving

Contact Info

Product

Resources

About