Comparability of Automated Vehicle Crash Databases
- URL: http://arxiv.org/abs/2308.00645v1
- Date: Tue, 1 Aug 2023 16:23:06 GMT
- Title: Comparability of Automated Vehicle Crash Databases
- Authors: Noah Goodall
- Abstract summary: Regulators and developers compare automated vehicle crash rates to baseline, human-driven crash rates.
Crash rates among databases may be directly comparable only with significant filtering and normalization, if at all.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Advanced driving assistance systems are available on many late-model
vehicles, and automated driving systems are testing on public roads. Regulators
and developers continue to assess the safety of these vehicles by comparing
automated vehicle crash rates to baseline, human-driven crash rates. While
there are several widely-cited automated vehicle and conventional vehicle crash
databases, these databases have different underlying assumptions and inclusion
criteria. Crash rates among databases may be directly comparable only with
significant filtering and normalization, if at all. This paper reviews current
automated vehicle and baseline human-driven crash databases and evaluates their
comparability. Recommendations are presented to improve their comparability,
both in terms of normalization and contextualization, as well as additional
data fields that can be incorporated into existing databases. These findings
may assist researchers, regulators, and automated vehicle developers attempting
to evaluate the safety of driving automation systems.
Related papers
- NsBM-GAT: A Non-stationary Block Maximum and Graph Attention Framework for General Traffic Crash Risk Prediction [11.444259609536164]
Existing crash risk prediction models rely on hypothetical scenarios deemed dangerous by researchers.
Dashcam videos capture the pre-crash behavior of individual vehicles, but they often lack critical information about the movements of surrounding vehicles.
We propose a novel non-stationary extreme value theory (EVT) to capture the interactive behavior between a vehicle and its surrounding vehicles.
arXiv Detail & Related papers (2025-03-06T02:12:40Z) - Enhancing Crash Frequency Modeling Based on Augmented Multi-Type Data by Hybrid VAE-Diffusion-Based Generative Neural Networks [13.402051372401822]
A key challenge in crash frequency modelling is the prevalence of excessive zero observations.
We propose a hybrid VAE-Diffusion neural network, designed to reduce zero observations.
We assess the synthetic data quality generated by this model through metrics like similarity, accuracy, diversity, and structural consistency.
arXiv Detail & Related papers (2025-01-17T07:53:27Z) - Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices.
This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
arXiv Detail & Related papers (2024-11-04T09:21:00Z) - GDFlow: Anomaly Detection with NCDE-based Normalizing Flow for Advanced Driver Assistance System [20.690653201455373]
We propose Graph Neural Controlled Differential Equation Normalizing Flow (GDFlow) to learn the distribution of normal driving patterns continuously.
We validate GDFlow using real-world electric vehicle driving data that we collected from Hyundai IONIQ5 and GV80EV.
arXiv Detail & Related papers (2024-09-09T06:04:41Z) - Methods to Estimate Advanced Driver Assistance System Penetration Rates in the United States [0.0]
This paper examines methods to estimate the proportion of vehicles equipped with advanced driver assistance systems (ADAS) in the United States.
In 2022, between 8% and 25% of vehicles were equipped with various ADAS features, though actual usage rates were lower due to driver deactivation.
Study proposes strategies to enhance estimates, including analyzing crash data, expanding event data recorder capabilities, conducting naturalistic driving studies, and collaborating with manufacturers to determine installation rates.
arXiv Detail & Related papers (2024-08-01T17:17:33Z) - Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses [76.59021017301127]
We propose a large-scale traffic crash language dataset, named CrashEvent, summarizing 19,340 real-world crash reports.
We further formulate the crash event feature learning as a novel text reasoning problem and further fine-tune various large language models (LLMs) to predict detailed accident outcomes.
Our experiments results show that our LLM-based approach not only predicts the severity of accidents but also classifies different types of accidents and predicts injury outcomes.
arXiv Detail & Related papers (2024-06-16T03:10:16Z) - Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs.
We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
arXiv Detail & Related papers (2024-05-14T09:42:21Z) - A Bayesian Approach for Prioritising Driving Behaviour Investigations in Telematic Auto Insurance Policies [0.6249768559720121]
We make use of trip GPS and accelerometer data, augmented by geospatial information, to train an imperfect classifier for delivery driving on a per-trip basis.
A posterior probability is converted to a priority score, which was used to select the most valuable candidates for manual investigation.
The approach has achieved a significant improvement in efficiency of human resource allocation compared to manual searching.
arXiv Detail & Related papers (2024-04-22T15:26:24Z) - Unsupervised Domain Adaptation for Self-Driving from Past Traversal
Features [69.47588461101925]
We propose a method to adapt 3D object detectors to new driving environments.
Our approach enhances LiDAR-based detection models using spatial quantized historical features.
Experiments on real-world datasets demonstrate significant improvements.
arXiv Detail & Related papers (2023-09-21T15:00:31Z) - Automated Automotive Radar Calibration With Intelligent Vehicles [73.15674960230625]
We present an approach for automated and geo-referenced calibration of automotive radar sensors.
Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles.
Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner.
arXiv Detail & Related papers (2023-06-23T07:01:10Z) - Driving Anomaly Detection Using Conditional Generative Adversarial
Network [26.45460503638333]
This study proposes an unsupervised method to quantify driving anomalies using a conditional generative adversarial network (GAN)
The approach predicts upcoming driving scenarios by conditioning the models on the previously observed signals.
The results are validated with perceptual evaluations, where annotators are asked to assess the risk and familiarity of the videos detected with high anomaly scores.
arXiv Detail & Related papers (2022-03-15T22:10:01Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object
Detection [55.12894776039135]
State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies.
We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain.
We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
arXiv Detail & Related papers (2021-03-26T01:18:11Z) - A model for traffic incident prediction using emergency braking data [77.34726150561087]
We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents.
We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
arXiv Detail & Related papers (2021-02-12T18:17:12Z) - Out-of-Distribution Detection for Automotive Perception [58.34808836642603]
Neural networks (NNs) are widely used for object classification in autonomous driving.
NNs can fail on input data not well represented by the training dataset, known as out-of-distribution (OOD) data.
This paper presents a method for determining whether inputs are OOD, which does not require OOD data during training and does not increase the computational cost of inference.
arXiv Detail & Related papers (2020-11-03T01:46:35Z) - Ethical Decision Making During Automated Vehicle Crashes [0.0]
Automated vehicles are expected to crash occasionally, even when all sensors, vehicle control components, and algorithms function perfectly.
This study investigates automated vehicle crashing and concludes the following: (1) automated vehicles will almost certainly crash, (2) an automated vehicle's decisions preceding certain crashes will have a moral component, and (3) there is no obvious way to effectively encode complex human morals in software.
arXiv Detail & Related papers (2020-10-30T14:58:17Z) - Self-awareness in Intelligent Vehicles: Experience Based Abnormality
Detection [4.721146043492144]
This paper introduces a novel method to detect abnormalities based on internal cross-correlation parameters of the vehicle.
It is possible to train a Dynamic Bayesian Network (DBN) model to automatically evaluate and detect when the vehicle is potentially misbehaving.
arXiv Detail & Related papers (2020-10-28T16:08:54Z) - Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised
Classification of Driving Behavior [4.640835690336653]
We present a semi-supervised learning solution to classify portions of trips according to whether drivers are driving aggressively or normally.
Our results show that, by utilizing RCGAN-generated labeled data, the classification of the drivers is improved in 79% of the cases.
arXiv Detail & Related papers (2020-06-16T15:49:21Z) - Interpretable Safety Validation for Autonomous Vehicles [44.44006029119672]
This work describes an approach for finding interpretable failures of an autonomous system.
The failures are described by signal temporal logic expressions that can be understood by a human.
arXiv Detail & Related papers (2020-04-14T21:11:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.