On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,932 papers · 148 categories

Trend · papers per month

count % of corpus

48 results for “Dynamic Driving Environment”

Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithmcs.RO

RL agent learns to smoothly change lanes in a dynamic driving environment.

problem Challenging lane change control with safety and comfort.

method Formulated continuous action for lane change in DDPG algorithm, defined reward function for learning.

result Successfully changed lanes with 100% success rate in diverse driving situations.

Reinforcement Learning Deep Deterministic Policy Gradient Lane Change Control Continuous Action Dynamic Driving Environment

2019-06-05abs ↗pdf ↗

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcs.RO

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.

autonomous driving deep reinforcement learning lane changing uncertain traffic reinforcement learning

2019-09-18abs ↗pdf ↗

Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learningcs.LG

Deep RL for multi-agent autonomous driving in dynamic environments.

problem Adapting to dynamic, multi-agent driving environments.

method Formulated Partially Observable Markov Games (POSG) for multi-agent learning.

result Demonstrated successful training of multi-agent control policies.

Deep Reinforcement Learning Multi-Agent Systems Autonomous Driving Markov Games Simulation Platforms

2019-11-11abs ↗pdf ↗

Learning Safety Constraints from Demonstrations with Unknown Rewardscs.LG

CoCoRL learns safe constraints from demonstrations with unknown rewards.

problem Learning safe constraints from demonstrations with different unknown rewards.

method Convex Constraint Learning for Reinforcement Learning (CoCoRL) constructs a convex safe set based on demonstrations.

result CoCoRL learns constraints that lead to safe driving behavior and can safely transfer to different tasks and environments.

Reinforcement Learning Safe Driving Constraint Learning Unknown Rewards Convex Optimization

2023-05-25abs ↗pdf ↗

Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environmentscs.RO

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.

self-driving safety meta-reinforcement lookahead non-stationary

2023-09-05abs ↗pdf ↗

Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Trafficcs.LG

Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penali…

2019-01-08abs ↗pdf ↗

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settingscs.LG

CARL safely adapts RL agents for safety-critical tasks.

problem Safety hazards in RL for safety-critical tasks.

method CARL combines model-based RL and cautious adaptation.

result CARL achieves higher rewards with fewer failures in safety-critical tasks.

Reinforcement Learning Safety-Critical Adaptation Risk-Aversion Model-Based

2020-08-15abs ↗pdf ↗

Foresee: Attentive Future Projections of Chaotic Road Environments with Online Trainingcs.LG

In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for example, in autonomous driving. Road environment is highly dynamic and complex due to the int…

2018-05-30abs ↗pdf ↗

Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Networkcs.NE

Deep Recurrent Q-Network improves autonomous driving in urban areas with pedestrians.

problem Challenges in urban autonomous driving due to complex road structures and unpredictable pedestrian behavior.

method Combines Deep Q-Network with LSTM for long-term memory, designed a 3-D state representation, and uses a reward function.

result The proposed DRQN-based approach outperforms rule-based methods in dense urban scenarios.

autonomous driving deep reinforcement learning urban environments pedestrians deep recurrent Q-network

2020-10-26abs ↗pdf ↗

Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneouslycs.LG

Paper tackles AI driving competition challenges with mixed simulation and real-world data.

problem AI algorithms perform poorly in real-world environments compared to simulated ones and vice versa.

method Employed imitation learning on a mixed dataset to train algorithms equally well in all environments.

result Trained algorithms performed well in both simulated and real-world environments.

AI Driving Olympics Imitation Learning Mixed Data Real-world Simulation

2020-07-07abs ↗pdf ↗

ApolloRL: a Reinforcement Learning Platform for Autonomous Drivingcs.RO

ApolloRL offers a platform for RL research in autonomous driving.

problem Improving reinforcement learning for autonomous driving.

method Open platform with training, simulation, and evaluation components.

result Baseline agents perform well in the ApolloRL environment.

ApolloRL Reinforcement Learning Autonomous Driving Open Platform Reinforcement Learning Platform

2022-01-29abs ↗pdf ↗

Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networkscs.LG

Paper predicts future vehicle trajectories for safer AVs.

problem Improving accuracy of long-term vehicle trajectory prediction for autonomous vehicles.

method Dual LSTM network for automatic learning of driver behaviors and future trajectory prediction.

result The method achieves lower RMSE for longitudinal and lateral predictions compared to state-of-the-art methods.

autonomous vehicles trajectory prediction driver behavior long short term memory sequence learning

2019-06-06abs ↗pdf ↗

Evolution of the distribution of wealth in an economic environment driven by local Nash equilibriaq-fin.GN

We present and analyze a model for the evolution of the wealth distribution within a heterogeneous economic environment. The model considers a system of rational agents interacting in a game theoretical framework, through fairly general assumptions on the cost function. This evolution drives the dynamic of the agents i…

2013-06-29abs ↗pdf ↗

Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learningcs.LG

Paper uses deep reinforcement learning for network slicing and traffic prediction.

problem Managing dynamic network slices while maintaining QoS in a 5G environment.

method Integrates LSTM for traffic prediction and DDRL for distributed decision-making.

result Significant improvements in network performance, reducing QoS violations.

network slicing deep reinforcement learning long short-term memory 5G dynamic environment

2024-01-12abs ↗pdf ↗

Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Carscs.LG

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.

deep reinforcement learning human-like driving collision avoidance self-driving cars mixed traffic

2020-06-07abs ↗pdf ↗

A machine learning environment for evaluating autonomous driving softwarecs.DC

A machine learning environment for detecting autonomous vehicle corner cases.

problem Testing autonomous driving software in the real world is difficult.

method Connecting CARLA simulation software to TensorFlow and custom AI client software.

result The system can identify situations where AI software fails to understand the scenario.

autonomous driving machine learning simulation corner cases AI testing

2020-03-07abs ↗pdf ↗

Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learningcs.RO

Deep learning agent improves pedestrian navigation in urban environments.

problem Autonomous driving among pedestrians in urban areas.

method Multi-objective deep reinforcement learning using a deep Q-learning variant.

result The multi-objective DQN agent outperforms single-objective DQN in various environments.

autonomous driving pedestrian navigation deep reinforcement learning multi-objective CARLA simulator

2021-10-11abs ↗pdf ↗

Generative One-Shot Learning (GOL): A Semi-Parametric Approach to One-Shot Learning in Autonomous Visioncs.CV

Highly Autonomous Driving (HAD) systems rely on deep neural networks for the visual perception of the driving environment. Such networks are trained on large manually annotated databases. In this work, a semi-parametric approach to one-shot learning is proposed, with the aim of bypassing the manual annotation step requ…

2018-12-19abs ↗pdf ↗

CORL: A Continuous-state Offset-dynamics Reinforcement Learnercs.LG

Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinforcementlearning algorithm for learning in these domains and prove for certain environments the algorithm is probably approximately correct wit…

2012-06-13abs ↗pdf ↗

Novelty Detection via Network Saliency in Visual-based Deep Learningcs.LG

Proposes novel method for detecting novel scenarios in autonomous systems.

problem Detecting when a machine learning model makes a trustworthy prediction in dynamic, real-world situations.

method Leverages trained model's learned information and a new image similarity metric.

result Demonstrates the method's efficacy on real-world driving and indoor racing datasets.

novelty detection deep learning autonomous systems image similarity saliency

2019-06-09abs ↗pdf ↗

Quadratic Q-network for Learning Continuous Control for Autonomous Vehiclescs.LG

A hybrid model combines Q-learning and PID controller for continuous vehicle control.

problem Learning unsatisfactory results with discrete action space in autonomous driving.

method Combining Q-learning and PID controller, using Quadratic Q-function approximation and action network.

result Autonomous vehicle successfully learns smooth and efficient driving behavior.

Reinforcement Learning Autonomous Driving Continuous Control PID Controller Quadratic Q-function

2019-11-29abs ↗pdf ↗

Learning a Directional Soft Lane Affordance Model for Road Scenes Using Self-Supervisioncs.CV

This work trains a model to predict human driving directions from road scenes.

problem Defining implicit rules of human behavior for autonomous vehicles.

method Self-supervised learning of probabilistic network model.

result Model successfully generalizes to new road scenes.

self-supervised learning probabilistic network road scenes driving behavior affordance modeling

2020-02-17abs ↗pdf ↗

Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Reviewcs.CV

Deep learning predicts vehicle behavior for safer autonomous driving.

problem Enhance autonomous vehicles' hazard awareness in complex environments.

method Review of deep learning-based approaches for vehicle behaviour prediction.

result Deep learning outperforms conventional methods in complex scenarios.

autonomous driving vehicle behavior prediction deep learning hazard awareness complex environments

2019-12-25abs ↗pdf ↗

Risk Measurement, Risk Entropy, and Autonomous Driving Risk Modelingq-fin.RM

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.

autonomous driving risk modeling risk management

2021-09-15abs ↗pdf ↗

IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Reportcs.CV

Top 8 robotic vision systems tackled lifelong object recognition challenges.

problem Lifelong learning in robotic vision for varied, dynamic environments.

method Design of a dataset with diverse conditions and rules for evaluation.

result Robotic vision systems improved over time with dynamic object appearances.

lifelong learning robotic vision object recognition dynamic environments illumination

2020-04-26abs ↗pdf ↗

Stochastic approximation in non-markovian environments revisitedstat.ML

Analyzes non-Markovian environments in stochastic approximation.

problem Understanding learning mechanisms in non-ergodic, non-Markovian settings.

method Analytic framework for transformer learning and continual learning.

result Proposes a new approach to transformer and continual learning.

stochastic approximation non-Markovian environments transformer learning continual learning

2026-03-22abs ↗pdf ↗

Deep Reinforcement Trading with Predictable Returnsq-fin.PM

Deep reinforcement learning improves trading performance with predictable returns.

problem Improving trading performance in financial markets with low signal-to-noise ratio.

method Investigates model-free deep reinforcement learning traders in a market with known mean-reverting factors.

result DRL agents outperform benchmarks in misspecified price dynamics and extreme events.

deep reinforcement learning portfolio optimization financial trading predictable returns mean-reverting factors

2021-04-29abs ↗pdf ↗

Budgeted Reinforcement Learning in Continuous State Spacecs.LG

A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemented in the shape of a cost signal constrained to lie below an - adjustable - threshold. So far, BMDPs could only be solved in the case of fi…

2019-03-03abs ↗pdf ↗

Relational Forward Models for Multi-Agent Learningcs.LG

The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predic…

2018-09-28abs ↗pdf ↗

Causal-based Time Series Domain Generalization for Vehicle Intention Predictioncs.LG

Proposes CTSDG model for better vehicle intention prediction across domains.

problem Domain generalization for vehicle intention prediction in dynamic environments.

method Structural causal model with recurrent latent variable integration.

result Consistent improvement in prediction accuracy compared to state-of-the-art methods.

vehicle intention prediction domain generalization structural causal model recurrent latent variable time series

2021-12-03abs ↗pdf ↗

Deep Reinforcement Learning framework for Autonomous Drivingstat.ML

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of…

2017-04-08abs ↗pdf ↗

Probabilistic Prediction of Vehicle Semantic Intention and Motioncs.LG

Accurately predicting the possible behaviors of traffic participants is an essential capability for future autonomous vehicles. The majority of current researches fix the number of driving intentions by considering only a specific scenario. However, distinct driving environments usually contain various possible driving…

2018-04-10abs ↗pdf ↗

On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networkscs.CV

Deep neural network learns compact representations for driving tasks.

problem Improving autonomous driving through better neural network representations.

method Inspired by human brain's hierarchical structure and predictive nature, the paper proposes a deep learning framework that learns compact representations of driving concepts.

result The paper successfully learns compact representations using as few as 16 neural units for car and lane concepts.

autonomous driving deep learning neural networks compact representations predictive models

2020-03-09abs ↗pdf ↗

Control-ITRA: Controlling the Behavior of a Driving Modelcs.AI

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.

driving behavior autonomous systems waypoint assignment target speed modulation realism

2025-01-17abs ↗pdf ↗

Meta learning Framework for Automated Drivingcs.AI

The success of automated driving deployment is highly depending on the ability to develop an efficient and safe driving policy. The problem is well formulated under the framework of optimal control as a cost optimization problem. Model based solutions using traditional planning are efficient, but require the knowledge …

2017-06-11abs ↗pdf ↗

Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learningcs.LG

We develop a robust RL algorithm for off-dynamics environments with improved suboptimality bounds and computational efficiency.

problem Learning policies robust to uncertainties in transition dynamics between training and deployment environments.

method Distributionally robust Markov decision processes (DRMDPs) with a novel algorithm We-DRIVE-U.

result Improved suboptimality bound of

\widetilde{\mathcal{O}}\big({d H \cdot \min \{1/ρ, H\}/\sqrt{K} }\big)

, near-optimal up to

\mathcal{O}(\sqrt{H})

Reinforcement Learning Distributionally Robust Off-Dynamics Markov Decision Processes Suboptimality Bounds

2024-09-30abs ↗pdf ↗

MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigationcs.LG

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.

autonomous driving multi-agent interaction reinforcement learning attention mechanism urban navigation

2020-08-17abs ↗pdf ↗

PhysVarMix: Physics-Informed Variational Mixture Model for Multi-Modal Trajectory Predictioncs.RO

PhysVarMix predicts diverse urban trajectories with physics constraints.

problem Predicting complex urban agent trajectories with multiple plausible scenarios.

method Physics-informed variational mixture model combining learning and physics constraints.

result Superior performance compared to existing methods on benchmark datasets.

trajectory prediction autonomous driving multi-modality physics-informed variational Bayesian

2025-07-25abs ↗pdf ↗

Adaptive Performance Assessment For Drivers Through Behavioral Advantagecs.LG

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 …

2018-04-23abs ↗pdf ↗

Interactive Teaching Algorithms for Inverse Reinforcement Learningcs.LG

Interactive teaching speeds up IRL learning with adaptive demonstrations.

problem Tackles the challenge of accelerating IRL learning with teacher assistance.

method Interactive teaching framework where a teacher adapts demonstrations based on the learner's policy.

result Teaching algorithms converge in the omniscient setting, speeding up learning.

Inverse Reinforcement Learning Interactive Teaching Adaptive Demonstrations Convergence Guarantees Car Driving Simulator

2019-05-28abs ↗pdf ↗

AuxNet: Auxiliary tasks enhanced Semantic Segmentation for Automated Drivingcs.CV

Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once considered a challenging task which is now becoming mature to be productized in a car. However, semanti…

2019-01-17abs ↗pdf ↗

Critic Sequential Monte Carlostat.ML

CriticSMC improves planning efficiency in constrained environments.

problem Planning with hard constraints in dynamic environments.

method Sequential Monte Carlo with learned heuristic factors.

result CriticSMC reduces collision rates with low computational cost.

Sequential Monte Carlo Soft-Q function planning inference hard constraints

2022-05-30abs ↗pdf ↗

Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Setscs.LG

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.

self-driving cars anticipation machine learning data-driven predictive driving

2019-10-17abs ↗pdf ↗

Reinforcement Learning based Control of Imitative Policies for Near-Accident Drivingcs.LG

H-ReIL learns to drive safely in near-accident scenarios.

problem Driving safely in high-risk near-accident situations.

method Hierarchical RL and IL approach.

result High-level policy switches between low-level policies for safe driving.

Reinforcement Learning Imitation Learning Autonomous Driving Near-Accident Scenarios Safety

2020-07-01abs ↗pdf ↗

Mining Personalized Climate Preferences for Assistant Drivingcs.LG

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.

driver behavior climate control IoT machine learning personalized preferences

2020-06-16abs ↗pdf ↗

CoverNet: Multimodal Behavior Prediction using Trajectory Setscs.LG

CoverNet predicts urban driving trajectories using diverse sets of possible actions.

problem Multimodal probabilistic trajectory prediction for urban driving.

method Frame trajectory prediction as classification over a diverse set of trajectories; dynamically generate sets based on current state.

result Outperforms state-of-the-art methods on real-world self-driving datasets.

trajectory prediction multimodal probabilistic urban driving self-driving

2019-11-23abs ↗pdf ↗

Towards Safer Self-Driving Through Great PAIN (Physically Adversarial Intelligent Networks)cs.LG

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.

self-driving adversarial training CARLA simulation dueling DDDQNs experience replay

2020-03-24abs ↗pdf ↗

Interpretable Long Term Waypoint-Based Trajectory Prediction Modelcs.AI

WayDCM predicts trajectories considering long-term goals, improving accuracy.

problem Predicting future trajectories of dynamic agents in complex environments.

method WayDCM combines DCM and NN to predict intermediate goals and trajectories, considering long-term goals.

result WayDCM outperforms previous methods on the Waymo Open dataset.

trajectory prediction long-term goals intermediate goals Waymo Open discrete choice model

2023-12-11abs ↗pdf ↗