Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction a…
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48 results for “Advanced Driver Assistance Systems”
DA-RNN predicts driving maneuvers up to 3 seconds ahead.
problem Adapting driving model to new drivers and vehicles.
method Domain-Adversarial Recurrent Neural Network (DA-RNN) for robust predictions.
result DA-RNN improves performance by 30% in real drivers and 114% in simulations.
DFKI Cabin Simulator tests visual monitoring functions in vehicles.
problem Validating novel human-vehicle interfaces and driver assistance systems.
method Driving simulator with in-cabin mock-up and camera system.
result Validation of in-cabin monitoring functions for advanced driver assistance and automated driving.
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.
The paper develops a faster surrogate model for simulators using hybrid methods.
problem The need for faster validation of automotive technologies using simulators.
method Testing classical methods and building hybrid models combining them.
result A hybrid surrogate model outperforms classical methods in multivariate time series prediction.
Adapts ISO 26262 for machine learning in automotive software.
problem Conflict between innovation and safety in automotive software development.
method Detailed assessment and adaptation of ISO 26262 for machine learning, analyzing and proposing new requirements.
result Adapted ISO 26262 for machine learning, addressing safety and innovation in automotive software.
Deep learning powers automotive innovations like self-driving cars.
problem Improving vehicle safety and functionality.
method Deployment of deep learning models in ADAS systems.
result Deep learning enhances performance and functionality of ADAS solutions.
Paper proposes personalized climate control for driver comfort.
problem Limited research on in-vehicle climate control and driver preferences.
method IoT platform for data collection, machine learning for driver behavior recognition, and personalized preference recommendation.
result Prototype demonstrates effective and accurate climate control for driver comfort.
LiDAR and Camera Detection Fusion in a Real Time Industrial Multi-Sensor Collision Avoidance Systemcs.CV
Industrial vehicle uses LiDAR and camera to detect and avoid restricted areas.
problem Avoiding collisions in industrial settings with automated vehicles.
method Combining LiDAR and camera data, using deep learning for projection, and model-predictive control.
result Reduces false positives in LiDAR detection of reflective beacons.
Improved pedestrian crossing prediction for AVs using contextual factors.
problem Accurate prediction of pedestrian crossing behavior for autonomous vehicles.
method Factored Latent-Dynamic Conditional Random Fields (FLDCRF) for multi-label sequence prediction and joint interaction modeling.
result Achieved at least 0.9 seconds earlier prediction accuracy for pedestrian crossing behavior compared to existing methods.
The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method …
System optimizes retrieval for personal assistants using reinforcement learning.
problem Needing a neural retrieval-based Q&A system for user memory.
method Direct optimization of F1-score using reinforcement learning.
result Improved retrieval performance on test sets.
The paper proposes a machine learning method to detect drivers' affective states using physiological signals.
problem Detecting and assessing drivers' affective states to improve driving safety and well-being.
method Multiview multi-task machine learning approach using physiological signals.
result Accounting for drive-specific differences significantly improves model performance.
Study improves vehicle motion prediction by incorporating traffic density.
problem Lack of contextual knowledge in predicting driving behavior.
method Categorize and define external conditions, evaluate motion prediction approach.
result Traffic density significantly improves motion prediction accuracy.
Survey examines challenges of ML in avionic systems certification.
problem Challenges in current certification standards for ML in avionic systems.
method Literature review focusing on robustness and explainability of ML results.
result Current certification standards do not support ML in avionic systems.
Builds a novel educational recommender for lifelong learners.
problem Challenges in creating scalable and transparent models for lifelong learning.
method Integrative approach combining content novelty, background knowledge, and learner engagement.
result TrueLearn achieves promising performance while retaining a human interpretable learner model.
This paper improves route choice models by incorporating contextual factors.
problem Existing route choice models lack consideration of dynamic contextual conditions.
method Knowledge distillation from Stated Choice Experiments in Immersive Virtual Environment.
result High-fidelity route choice models with increased predictive power.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
AI analyzes corporate ESG filings to identify key dimensions and investor reactions.
problem Lack of reliable ESG ratings systems in corporate filings.
method AI techniques to separate and measure ESG dimensions and investor responses.
result AI can improve ESG ratings systems by identifying key dimensions and investor reactions.
Extended CSGE improves power and cyclist movement forecasting.
problem Power and cyclist movement forecasting challenges.
method Extended Coopetitive Soft Gating Ensemble (XCSGE) with flexible weighting.
result Improves prediction performance by up to 30% for solar power forecasting.
Optimizes portfolios by identifying causal drivers of diversification.
problem Achieving efficient portfolio optimization based on asset and diversification dynamics.
method Commonality Principle, Reichenbach Common Cause Principle, conformal maps, Bayesian networks, correlation-based algorithms, neural networks, SDEs.
result Optimal portfolio diversification achieved through causal methodologies and sensitivity forecasting.
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.
SafeRNet: Safe Transportation Routing in the era of Internet of Vehicles and Mobile Crowd Sensingcs.LG
World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of …
Deep RL tackles fleet management and dispatching for ride-sharing platforms.
problem Optimizing dispatching and repositioning of drivers in ride-sharing platforms.
method Deep reinforcement learning approach treating drivers as a central system agent.
result Centralized decision-making improves overall fleet efficiency.
This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.
problem Unserved passenger requests due to drivers' cruising behavior during passenger seeking.
method Mean field multi-agent reinforcement learning (MARL) with a reward design scheme and Bayesian optimization (BO) to solve bilevel optimization problems.
result Optimal toll charges and service charges can improve platform and city planner objectives by significant margins, leading to better traffic conditions.
As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In thi…
A scoring method for driving safety using trajectory data.
problem Managing traffic safety through driver behaviors and violations.
method Extract driving habits and violations from trajectories, train a model, score drivers.
result Proves the effectiveness of the scoring method using traffic simulation.
A deep learning system detects more types of driver distractions with high accuracy.
problem Detecting various types of driver distractions to reduce road accidents.
method Genetically-weighted ensemble of convolutional neural networks.
result Achieves 90% accuracy in detecting more types of distractions than existing methods.
A new method predicts non-Markovian closure terms for complex systems.
problem Predicting the effect of unresolved variables on resolved dynamics in high-dimensional systems.
method Mamba-Assisted Closure (MAC) framework: sequence model trained to predict closure from resolved trajectory, coupled with reduced-order equations.
result Substantially outperforms existing methods in predictive accuracy and long-time stability.
Deep RL agent improves lane changing in unpredictable traffic.
problem Uncertainty in other drivers' behaviors and safety vs agility trade-off.
method Developed a deep reinforcement learning agent in a simulated highway environment.
result Significantly better performance in noisy environments compared to heuristic methods.
System identifies and responds to help queries in personal assistants.
problem Difficulty in remembering command structures for various tasks in personal assistants.
method Proposes a C-BiLSTM based classifier and semantic ANN module to detect and respond to help queries.
result System outperforms other approaches in returning relevant responses for help queries.
We consider the problem of numerical approximation for forward-backward stochastic differential equations with drivers of quadratic growth (qgFBSDE). To illustrate the significance of qgFBSDE, we discuss a problem of cross hedging of an insurance related financial derivative using correlated assets. For the convergence…
Improved accuracy in a commercial assistant by smartly selecting new training data.
problem Expensive and time-consuming to annotate new data for ML systems.
method Automatically identifies new helpful examples suitable for human annotation.
result The proposed method leads to higher accuracy improvements with a fixed annotation budget.
Synthetic telematics dataset created from insurance claims data.
problem Creating a reliable synthetic dataset for usage-based insurance models.
method Three-stage process using machine learning: binary classification, regression, and synthetic oversampling.
result Synthetic dataset remarkably similar to real data in risk assessment models.
New formula for portfolio risk management using conditional PDEs.
problem Optimal diversification and risk management of portfolios.
method Closed-form formula for conditional probability, Gaussian copulas, conditional risk-neutral PDE.
result Dynamic monitoring of portfolio volatilities and weights from PDEs.
AI agent improves performance attribution analysis with high accuracy.
problem Improving accuracy in performance attribution analysis.
method Leveraging large language models and advanced prompt engineering techniques.
result Achieves accuracy rates exceeding 93% in analyzing performance drivers.
Study develops sign recognition system for DHH users.
problem Accessibility of voice-controlled devices for Deaf and Hard-of-Hearing users.
method Multimodal data (RGB video and skeletal data) for sign language recognition using deep learning.
result Validation on GMUASL51 dataset of 12 users and 13107 samples across 51 signs.
SusGen-GPT improves financial NLP and ESG report generation.
problem Lack of advanced NLP tools for finance and ESG domains.
method Developed SusGen-30K dataset and SusGen-GPT models.
result Achieved state-of-the-art performance in financial NLP tasks.
TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resourcescs.AI
TrueLearn uses Bayesian algorithms to match learners with educational resources.
problem Matching lifelong learners to open educational resources efficiently and effectively.
method TrueLearn uses a text ontology and Bayesian strategies to match learners with educational resources based on their background knowledge and material novelty.
result TrueLearn algorithms show promise in building an effective educational recommendation system.
New approach predicts crime and terrorism events with high accuracy and transparency.
problem Predicting crime and terrorism events with high accuracy and interpretability.
method Granger Network inference for local transport rules learning.
result Achieved AUC of ~90% for crime predictions and ~80% for terrorism predictions.
Improved investment performance with fine-grained LLM tasks.
problem Abstract financial trading systems often overlook real-world workflow intricacies, leading to degraded performance.
method Proposes a multi-agent LLM trading framework that decomposes investment analysis into fine-grained tasks.
result Fine-grained task decomposition significantly improves risk-adjusted returns compared to coarse-grained designs.
Le trading algorithmiqueq-fin.TR
The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on trans…
Mathematical advances needed for Digital Twins, differing from traditional models.
problem Foundational mathematical advances required for Digital Twins.
method Multi-scale, multi-physics modeling and coupling, different reliability criteria and uncertainty assessments.
result AI/ML methods can perform well in biomedical problems but fail in simple engineering systems.
OCC system speeds up in-app communications for Uber drivers and riders.
problem Improving efficiency in in-app messaging between drivers and riders.
method Intents are detected using unsupervised embedding and nearest-neighbor classifier. Replies are retrieved based on popularity in historical data.
result System achieves 76% accuracy in intent detection and 71% adoption rate of smart replies.
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely believed that growing training sets and models should improve accuracy and result in better products. As DL application domains grow, we would li…
Paper presents CeNN quantization for efficient CPS applications.
problem Efficient processing for CPS applications, especially in telemedicine and ADAS.
method Incremental quantization of CeNNs with various strategies.
result Achieved up to 7.8x speedup with no performance loss.
Neural Assistant integrates knowledge reasoning and dialogue generation in a single model.
problem Challenges in task-oriented dialog systems, including multi-turn language understanding and generation, knowledge retrieval and reasoning, and action prediction.
method Develops a single neural network model that jointly predicts text responses and actions from conversation history and external knowledge.
result The model learns to reason on external knowledge with weak supervision, improving factual accuracy and language generation performance.
Paper tackles modeling TVCNs with unknown drivers and partial data.
problem Modeling and understanding TVCNs with unknown drivers and partial data.
method Generalized modeling approach of TVCNs using fractional dynamical equations and iterative framework.
result Proposed framework successfully predicts state of the system using real EEG data.