Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety…
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48 results for “Aggressive Driving”
Machine learning detects road anomalies and aggressive driving from smartphone data.
problem Road quality assessment and aggressive driving detection.
method Machine learning techniques applied to smartphone acceleration data.
result Robust platform for road transport evaluation.
Driving styles have a great influence on vehicle fuel economy, active safety, and drivability. To recognize driving styles of path-tracking behaviors for different divers, a statistical pattern-recognition method is developed to deal with the uncertainty of driving styles or characteristics based on probability density…
Extends driving model to control agent behavior in simulations.
problem Simulate realistic driving behavior for autonomous systems.
method Introduces Control-ITRA method to influence agent behavior through waypoint assignment and target speed modulation.
result Demonstrates controllable, infraction-free trajectories while preserving realism.
Researchers develop PAIN to improve self-driving safety through adversarial training.
problem Overfitting and poor generalizability of neural networks in self-driving vehicles.
method PAIN combines adversarial training in CARLA simulation to generate edge cases.
result Trained self-driving vehicles are more resilient to environmental uncertainty and less prone to collisions.
A Rapid Pattern-Recognition Method for Driving Types Using Clustering-Based Support Vector Machinesstat.ML
A rapid pattern-recognition approach to characterize driver's curve-negotiating behavior is proposed. To shorten the recognition time and improve the recognition of driving styles, a k-means clustering-based support vector machine ( kMC-SVM) method is developed and used for classifying drivers into two types: aggressiv…
MIDAS learns to adaptively control other cars in urban driving scenarios.
problem Autonomous vehicles need to interact with other agents on the road.
method Reinforcement learning with attention mechanism to handle multiple agents.
result MIDAS policies are adaptive and robust to external changes.
Self-training with noisy student-teacher boosts keyword spotting accuracy.
problem Robust keyword spotting in challenging conditions.
method Aggressive data augmentation and self-training with noisy student-teacher approach.
result Significant accuracy improvement in difficult conditions, up to 60%.
The diagonal effect of orders is well documented in different markets, which states that orders are more likely to be followed by orders of the same aggressiveness and implies the presence of short-term correlations in order flows. Based on the order flow data of 43 Chinese stocks, we investigate if there are long-rang…
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings,…
Long-term lane change prediction model predicts maneuvers with 75% accuracy.
problem Predicting long-term lane changes for safer autonomous driving.
method Introduced three models: logistic regression, MLP, and RNN. Used NGSIM dataset with new labeling scheme.
result Developed model predicts 75% of lane changes with an average advanced time of 8.05 seconds.
We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the b…
The last financial and economic crisis demonstrated the dysfunctional long-term effects of aggressive behaviour in financial markets. Yet, evolutionary game theory predicts that under the condition of strategic dependence a certain degree of aggressive behaviour remains within a given population of agents. However, as …
Real-time semantic segmentation for autonomous vehicles on FPGA reduces latency and power consumption.
problem Efficient real-time semantic segmentation for autonomous vehicles.
method Compressed ENet architecture, FPGA deployment, batch processing, filter reduction, quantization-aware training.
result Reduced latency to 3 ms per image with batch size of ten and 40% resource utilization.
Price changes are induced by aggressive market orders in stock market. We introduce a bivariate marked Hawkes process to model aggressive market order arrivals at the microstructural level. The order arrival intensity is marked by an exogenous part and two endogenous processes reflecting the self-excitation and cross-e…
New online learning algorithm combines PA and TER for binary classification.
problem Binary classification with non-separable data and data imbalance.
method Online Passive-Aggressive (PA) and Total-Error-Rate (TER) learning combined into PATER algorithm.
result PATER algorithms outperform existing online learning algorithms in efficiency and effectiveness.
Stabilizes online learning by using weighted reservoir sampling.
problem Real-world deployment sensitivity to outliers causes low accuracy in final solutions.
method Weighted reservoir sampling to stabilize ensemble model without additional data passes.
result Risk of ensemble classifier is bounded with respect to the underlying online learning method's regret.
A method learns common bias for multiple low-variance tasks without hyper-parameter tuning.
problem Learning common bias for multiple low-variance tasks without manual tuning.
method Two variants of online learning methods (aggressive and lazy) that update bias after each datapoint or at the end of each task.
result Across-tasks regret bound derived for the method, showing faster rates for aggressive variant and standard rates for lazy variant.
Models predict order book recovery from aggressive trading follows a simple t^1/3 scaling.
problem Understanding order book dynamics in financial markets.
method Developed macroscopic models and solved numerically and asymptotically.
result Order book recovery follows a t^1/3 scaling law.
New theory shows alliances neither deter nor provoke aggression.
problem The causal effect of alliances on conflict is unclear due to endogeneity issues.
method Innovative generalized joint regression models to account for endogeneity.
result Alliances neither deter nor provoke aggression.
Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most aggressive such practices, and arrive at figures that are surprisingly modest. By "aggressive" we mean any trading …
We propose a general framework to describe the impact of different events in the order book, that generalizes previous work on the impact of market orders. Two different modeling routes can be considered, which are equivalent when only market orders are taken into account. One model posits that each event type has a te…
Paper uses stats to predict treatment choice based on illness probability.
problem Improving treatment decision-making in personalized medicine.
method Statistical decision theory with maximum regret evaluation.
result Estimates illness probability for better treatment choice.
A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermo…
CSER improves SGD efficiency by resetting errors and partial synchronization.
problem Limited scalability of Distributed Stochastic Gradient Descent (SGD) due to communication bottlenecks.
method Introduces 'error reset' technique and partial synchronization for gradients and models.
result Proves convergence for smooth non-convex problems and accelerates distributed training significantly.
New research shows IBM's GDX algorithm outperforms Vytelingum's Adaptive-Aggressive strategy in market simulations.
problem Comparing the performance of adaptive-aggressive trading algorithms in various market scenarios.
method Exhaustive testing across a wide range of market environments using large-scale compute facilities.
result Vytelingum's Adaptive-Aggressive strategy is consistently outperformed by IBM's GDX algorithm in simple market conditions.
Online Passive-Aggressive (PA) learning is a class of online margin-based algorithms suitable for a wide range of real-time prediction tasks, including classification and regression. PA algorithms are formulated in terms of deterministic point-estimation problems governed by a set of user-defined hyperparameters: the a…
In order-driven markets, limit-order book (LOB) resiliency is an important microscopic indicator of market quality when the order book is hit by a liquidity shock and plays an essential role in the design of optimal submission strategies of large orders. However, the evolutionary behavior of LOB resilience around liqui…
A novel framework interprets driving patterns using Action phases clustering.
problem Challenges in comprehending driving heterogeneity from underlying behavior mechanisms.
method Resampling and Downsampling Method (RDM) followed by iterative clustering calibration.
result Six driving patterns identified in real-world datasets, revealing dynamic nature of driving.
In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Mo…
The kind of realized mission inflows the sensitivity to risk. Among other factors, the risk results from decision about liquid assets investment level and liquid assets financing. The higher the risk exposure, the higher the level of liquid assets. If the specific risk exposure is smaller, the more aggressive could be …
Deep RL mimics human driving for collision avoidance in self-driving cars.
problem Developing human-like driving policies for autonomous vehicles in mixed traffic environments.
method Model-free, deep reinforcement learning approach using a combination of rule-based and expert-driven data.
result Demonstrated human-like driving policies through Gaussian process modeling of track position and speed distributions.
Investors' strategies in a market influenced by price impact are analyzed, showing aggressive behavior when impact exceeds a critical point.
problem Strategic interaction and Nash equilibria of investors in a financial market with price impact.
method Analysis of Nash equilibria for relative investors with CRRA and CARA utility functions in a Brownian motion-driven market, considering both linear and non-linear price impacts.
result Investors' aggressive behavior is observed when price impact exceeds a critical parameter.
Automobile theft detection method uses owner driving data clustering.
problem Automobile theft detection challenges with limited real data.
method Clustering owner driving data using k-means algorithm, then detecting theft by comparing reconstructed data.
result 99% accuracy in detecting vehicle theft using only owner driving data.
Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's decision-making design. This paper presents a framework of analyzing various encountering behaviors through decomposing driving encounter data …
The paper introduces a new insurance pricing model based on driving mileage.
problem Weak link between insurance premiums and mileage, leading to overdriving and accidents.
method Developed a Pay-As-You-Drive insurance pricing model using a counting process and non-homogeneous Poisson distribution.
result The model provides theoretical results for better insurance pricing based on driving behavior.
Detecting aggressive cancer tumors using ctDNA dynamics from few blood samples.
problem Early multi-cancer detection using circulating tumor DNA (ctDNA) levels.
method Combines continuous time Markov modelling and Signature theory for efficient testing procedures.
result Correctly addresses the challenge of data scarcity in cancer monitoring.
New method preserves spectral clustering performance under aggressive sparsification and quantization.
problem Maintaining spectral clustering performance with sparse and quantized data.
method Random matrix theory applied to eigenspectrum changes under sparsification and quantization.
result Spectral clustering performance is preserved even with aggressive sparsification and quantization.
Automatic Identification of Driving Maneuver Patterns using a Robust Hidden Semi-Markov Modelsstat.ML
New method reduces state redundancy in HSMM for driving patterns.
problem Overestimation of states in HSMM models.
method Robust HDP-HSMM (rHDP-HSMM) method to reduce redundant states.
result Improved consistency and accurate inference of driving maneuvers.
This paper presents a novel approach for automatic rule learning applicable to an autonomous driving system using real driving data.
The study analyzes how large language models form and express investor risk profiles.
problem Understanding how large language models (LLMs) form and express investor risk profiles.
method Examined three LLMs (GPT, Gemini, and Llama) and assessed their responses to a standardized risk questionnaire under varying prompts.
result LLMs generally form long-term investment profiles, but they exhibit different risk tolerance levels.
The paper explores new risk models for autonomous driving.
problem Risk management and actuarial modeling for autonomous vehicles.
method Examines technical difficulties and proposes a novel risk model.
result The new model better reflects real-world driving safety.
CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarioscs.LG
CMTS synthesizes near-miss driving scenarios for safer autonomous driving tests.
problem Lack of near-miss driving data for testing autonomous driving algorithms.
method Generative model conditioned on road maps, using Variational Bayesian methods.
result Synthesized data covers more near-miss scenarios, improving trajectory prediction and risk handling.
Automated vehicles learn to predict upcoming maneuvers with high accuracy.
problem Making self-driving cars feel safer by anticipating future actions.
method Machine learning techniques applied to a large dataset of real-world driving.
result Automated vehicles can predict maneuvers up to 5 seconds in advance with high accuracy.
In this paper, we propose exact passive-aggressive (PA) online algorithms for learning to rank. The proposed algorithms can be used even when we have interval labels instead of actual labels for examples. The proposed algorithms solve a convex optimization problem at every trial. We find exact solution to those optimiz…
The paper examines how random seed affects model stability and proposes ASWA and NASWA techniques to improve model robustness.
problem The impact of random seed on model performance and stability.
method A controlled study on attention, gradient-based, and surrogate model interpretations. Proposed techniques: Aggressive Stochastic Weight Averaging (ASWA) and Norm-filtered Aggressive Stochastic Weight Averaging (NASWA).
result Improvement in model robustness with ASWA and NASWA techniques, reducing standard deviation of model performance by 72%.
Conformal Candidate Certification advances offline MBO by certifying candidate designs with statistical guarantees.
problem Offline model-based optimization
method Conformal Candidate Certification (CCC)
result CCC certifies 16.7% of an aggressive proposal pool with 0.990 empirical coverage at nominal 0.90.
Study improves self-driving safety in dynamic environments.
problem Safe self-driving in non-stationary urban settings.
method Neurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA).
result Self-driving agents can adapt safely in real-time.