23 research outputs found
Using simulation to quantify the performance of automotive perception systems
The design and evaluation of complex systems can benefit from a software
simulation - sometimes called a digital twin. The simulation can be used to
characterize system performance or to test its performance under conditions
that are difficult to measure (e.g., nighttime for automotive perception
systems). We describe the image system simulation software tools that we use to
evaluate the performance of image systems for object (automobile) detection. We
describe experiments with 13 different cameras with a variety of optics and
pixel sizes. To measure the impact of camera spatial resolution, we designed a
collection of driving scenes that had cars at many different distances. We
quantified system performance by measuring average precision and we report a
trend relating system resolution and object detection performance. We also
quantified the large performance degradation under nighttime conditions,
compared to daytime, for all cameras and a COCO pre-trained network
FIR-based Future Trajectory Prediction in Nighttime Autonomous Driving
The performance of the current collision avoidance systems in Autonomous
Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) can be drastically
affected by low light and adverse weather conditions. Collisions with large
animals such as deer in low light cause significant cost and damage every year.
In this paper, we propose the first AI-based method for future trajectory
prediction of large animals and mitigating the risk of collision with them in
low light. In order to minimize false collision warnings, in our multi-step
framework, first, the large animal is accurately detected and a preliminary
risk level is predicted for it and low-risk animals are discarded. In the next
stage, a multi-stream CONV-LSTM-based encoder-decoder framework is designed to
predict the future trajectory of the potentially high-risk animals. The
proposed model uses camera motion prediction as well as the local and global
context of the scene to generate accurate predictions. Furthermore, this paper
introduces a new dataset of FIR videos for large animal detection and risk
estimation in real nighttime driving scenarios. Our experiments show promising
results of the proposed framework in adverse conditions. Our code is available
online.Comment: Conference: IEEE Intelligent Vehicles 2023 (IEEE IV 2023
Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention
Driver Monitoring Systems (DMSs) are crucial for safe hand-over actions in
Level-2+ self-driving vehicles. State-of-the-art DMSs leverage multiple sensors
mounted at different locations to monitor the driver and the vehicle's interior
scene and employ decision-level fusion to integrate these heterogenous data.
However, this fusion method may not fully utilize the complementarity of
different data sources and may overlook their relative importance. To address
these limitations, we propose a novel multiview multimodal driver monitoring
system based on feature-level fusion through multi-head self-attention (MHSA).
We demonstrate its effectiveness by comparing it against four alternative
fusion strategies (Sum, Conv, SE, and AFF). We also present a novel
GPU-friendly supervised contrastive learning framework SuMoCo to learn better
representations. Furthermore, We fine-grained the test split of the DAD dataset
to enable the multi-class recognition of drivers' activities. Experiments on
this enhanced database demonstrate that 1) the proposed MHSA-based fusion
method (AUC-ROC: 97.0\%) outperforms all baselines and previous approaches, and
2) training MHSA with patch masking can improve its robustness against
modality/view collapses. The code and annotations are publicly available.Comment: 9 pages (1 for reference); accepted by the 6th Multimodal Learning
and Applications Workshop (MULA) at CVPR 202
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center
Autoencoders have been extensively used in the development of recent anomaly
detection techniques. The premise of their application is based on the notion
that after training the autoencoder on normal training data, anomalous inputs
will exhibit a significant reconstruction error. Consequently, this enables a
clear differentiation between normal and anomalous samples. In practice,
however, it is observed that autoencoders can generalize beyond the normal
class and achieve a small reconstruction error on some of the anomalous
samples. To improve the performance, various techniques propose additional
components and more sophisticated training procedures. In this work, we propose
a remarkably straightforward alternative: instead of adding neural network
components, involved computations, and cumbersome training, we complement the
reconstruction loss with a computationally light term that regulates the norm
of representations in the latent space. The simplicity of our approach
minimizes the requirement for hyperparameter tuning and customization for new
applications which, paired with its permissive data modality constraint,
enhances the potential for successful adoption across a broad range of
applications. We test the method on various visual and tabular benchmarks and
demonstrate that the technique matches and frequently outperforms alternatives.
We also provide a theoretical analysis and numerical simulations that help
demonstrate the underlying process that unfolds during training and how it can
help with anomaly detection. This mitigates the black-box nature of
autoencoder-based anomaly detection algorithms and offers an avenue for further
investigation of advantages, fail cases, and potential new directions.Comment: 16 pages, 4 figures, 4 table
Effect of BMI on maximum oxygen uptake of high risk individuals in a population of eastern Uttar Pradesh
Background: Herein, we report the effect of body mass index (BMI) on respiratory fitness by measuring maximum oxygen uptake after a short-term aerobic exercise. 20-40 years old healthy male individuals were divided into three categories according to their BMI and asked to perform aerobic exercise on a treadmill. Maximum oxygen uptake (VO2max) immediately after the exercise was recorded and statistically analyzed. Materials and methods: Thirty individuals of age within the range of 20-40 years were first medically examined to be certain that they did not have any cardiorespiratory complications and their BMI was calculated. Based on their BMI, they were classified into three—normal, overweight and obese groups and subjected to a treadmill exercise as per Bruce Protocol. Recorded data were analyzed and student t-test was performed to test significance of the data. Result: It was observed that the VO2max decreases as BMI increases. Conclusion: This study establishes a correlation between maximum oxygen uptake and BMI of individuals that suggests that with increased BMI, VO2max decreases resulting into a decrease in respiratory fitness level. This trend was found to be consistent among all normal, overweight and obese group individuals. There are numerous reports on respiratory endurance where individuals were subjected to aerobic exercises over a long period of time. Here, we have studied the immediate effect of an aerobic exercise on the maximum oxygen uptake of normal and high risk individuals who were not subjected to long term exercises for respiratory endurance
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis
In the automotive industry, the full cycle of managing in-use vehicle quality
issues can take weeks to investigate. The process involves isolating root
causes, defining and implementing appropriate treatments, and refining
treatments if needed. The main pain-point is the lack of a systematic method to
identify causal relationships, evaluate treatment effectiveness, and direct the
next actionable treatment if the current treatment was deemed ineffective. This
paper will show how we leverage causal Machine Learning (ML) to speed up such
processes. A real-word data set collected from on-road vehicles will be used to
demonstrate the proposed framework. Open challenges for vehicle quality
applications will also be discussed.Comment: Accepted by NeurIPS 2022 Workshop on Causal Machine Learning for
Real-World Impact (CML4Impact 2022
Robust multiview multimodal driver monitoring system using masked multi-head self-attention
No abstract available