Arnav Malawade - Academia.edu (original) (raw)

Papers by Arnav Malawade

Although autonomous vehicles (AVs) are expected to revolutionize transportation, robust perceptio... more Although autonomous vehicles (AVs) are expected to revolutionize transportation, robust perception across a wide range of driving contexts remains a significant challenge. Techniques to fuse sensor data from camera, radar, and lidar sensors have been proposed to improve AV perception. However, existing methods are insufficiently robust in difficult driving contexts (e.g., bad weather, low light, sensor obstruction) due to rigidity in their fusion implementations. These methods fall into two broad categories: (i) early fusion, which fails when sensor data is noisy or obscured, and (ii) late fusion, which cannot leverage features from multiple sensors and thus produces worse estimates. To address these limitations, we propose HydraFusion: a selective sensor fusion framework that learns to identify the current driving context and fuses the best combination of sensors to maximize robustness without compromising efficiency. HydraFusion is the first approach to propose dynamically adjusti...

Autonomous vehicles use multiple sensors, large deep-learning models, and powerful hardware platf... more Autonomous vehicles use multiple sensors, large deep-learning models, and powerful hardware platforms to perceive the environment and navigate safely. In many contexts, some sensing modalities negatively impact perception while increasing energy consumption. We propose EcoFusion: an energy-aware sensor fusion approach that uses context to adapt the fusion method and reduce energy consumption without affecting perception performance. EcoFusion performs up to 9.5% better at object detection than existing fusion methods with approximately 60% less energy and 58% lower latency on the industry-standard Nvidia Drive PX2 hardware platform. We also propose several context-identification strategies, implement a joint optimization between energy and performance, and present scenario-specific results.

Proceedings 2019 Network and Distributed System Security Symposium, 2019

There is considerable evidence that evaluating the subjective risk level of driving decisions can... more There is considerable evidence that evaluating the subjective risk level of driving decisions can improve the safety of Autonomous Driving Systems (ADS) in both typical and complex driving scenarios.In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers.Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers.To train our model, we formulate the problem of subjective risk assessment as a supervised scene classification problem.We evaluate our model on both synthetic lane-changing datasets and real-driving datasets with various driving maneuvers.We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4\% vs. 91.2\%) and small (91.8\% vs. 71.2\%) lane-changing synthesized datasets, illustrating that our approach can learn effectively even from small d...

This dataset contains multimodal sensor data collected from side-channels while printing several ... more This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer. By utilizing multiple side-channels, we improve system state estimation significantly in comparison to uni-modal techniques. Besides, in the paper we analyze the value of each side-channel for performing attack detection in terms of mutual information shared with the machine control parameters. We evaluate our system on real-world test cases and achieve an attack detection accuracy of 98.15%. Our dataset contains sets of G-codes synchronized with the corresponding sensor readings and sensor features, enabling highly accurate state esti...

Knowledge-Based Systems, 2022

IEEE Transactions on Intelligent Transportation Systems, 2021

2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), 2020

ArXiv, 2021

Cyber-Physical Additive Manufacturing (AM) constructs a physical 3D object layer-by-layer accordi... more Cyber-Physical Additive Manufacturing (AM) constructs a physical 3D object layer-by-layer according to its digital representation and has been vastly applied to fast prototyping and the manufacturing of functional end-products across fields. The computerization of traditional production processes propels these technological advancements; however, this also introduces new vulnerabilities, necessitating the study of cyberattacks on these systems. The AM Sabotage Attack is one kind of kinetic cyberattack that originates from the cyber domain and can eventually lead to physical damage, injury, or even death. By introducing inconspicuous yet damaging alterations in any specific process of the AM digital process chain, the attackers can compromise the structural integrity of a manufactured component in a manner that is invisible to a human observer. If the manufactured objects are critical for their system, those attacks can even compromise the whole system's structural integrity and ...

ACM Transactions on Embedded Computing Systems, 2021

Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety sig... more Autonomous vehicles (AV) are expected to revolutionize transportation and improve road safety significantly. However, these benefits do not come without cost; AVs require large Deep-Learning (DL) models and powerful hardware platforms to operate reliably in real-time, requiring between several hundred watts to one kilowatt of power. This power consumption can dramatically reduce vehicles’ driving range and affect emissions. To address this problem, we propose SAGE: a methodology for selectively offloading the key energy-consuming modules of DL architectures to the cloud to optimize edge, energy usage while meeting real-time latency constraints. Furthermore, we leverage Head Network Distillation (HND) to introduce efficient bottlenecks within the DL architecture in order to minimize the network overhead costs of offloading with almost no degradation in the model’s performance. We evaluate SAGE using an Nvidia Jetson TX2 and an industry-standard Nvidia Drive PX2 as the AV edge, device...

Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020

Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existin... more Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach or an application is much more complicated. Many publications leverage multiple modalities to convey their findings and spread their ideas - they include pseudocode, tables, images and diagrams in addition to text, and often make publicly accessible their implementations. It is important to be able to represent and query them as well. We utilize RDF Knowledge graphs (KGs) to represent multimodal information and enable expressive querying over modalities. In our demo we present an approach for extracting KGs from different modalities, namely text, architecture images and source code. We show how graph queries can be used to get insights into different facets (modalities) of a paper, and its associated code implementation. Our innovation lies in the multimodal nature of the KG we create. While our work is of direct interest to DL researchers and practitioners, our approaches can also be leveraged in other scientific domains.

IEEE Transactions on Industrial Informatics, 2021

IEEE Transactions on Intelligent Transportation Systems, 2020

IEEE Internet of Things Journal, 2022