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.
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.
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.
Comma.ai's approach to Artificial Intelligence for self-driving cars is based on an agent that learns to clone driver behaviors and plans maneuvers by simulating future events in the road. This paper illustrates one of our research approaches for driving simulation. One where we learn to simulate. Here we investigate v…
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.
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.
Deep learning speeds up engine calibration for varied driving conditions.
problem Optimizing engine operation during transient driving cycles for better fuel economy and emissions.
method Parallel simulation-driven machine learning using a physics-based engine simulator.
result Deep neural network surrogate model predicts engine performance and emissions accurately and quickly.
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.
ACOL learns constraints from human preferences in driving simulations.
problem Learning constraints from human preferences in driving simulations.
method Adaptive Constraint Learning (ACOL) algorithm for constrained linear best-arm identification.
result ACOL's sample complexity matches worst-case lower bound and is significantly tighter in the average case.
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.
End-to-end autonomous driving perception learns latent features for better performance.
problem Current autonomous driving systems are complex and require human engineering.
method Sequential latent representation learning for end-to-end perception.
result End-to-end perception model solves detection, tracking, localization, and mapping problems.
End-to-end framework classifies cognitive workload in real-time driving scenarios.
problem Challenging task of classifying human cognitive states from behavioral and physiological signals.
method End-to-end framework using mixture Hyper Long Short Term Memory Networks (HyperNetworks).
result Framework outperforms previous methods with 83.9% precision and 87.8% recall.
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.
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.
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.
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.
A deep reinforcement learning method with rule-based constraints improves safe and efficient lane changes in autonomous driving.
problem Complex and uncertain traffic environment challenges autonomous driving decision-making.
method Deep Q-Network (DQN) combined with rule-based constraints for lane change decision-making.
result The proposed rule-based DQN method outperforms both rule-based and DQN approaches in a real-world simulator.
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.
Learning to drive faithfully in highly stochastic urban settings remains an open problem. To that end, we propose a Multi-task Learning from Demonstration (MT-LfD) framework which uses supervised auxiliary task prediction to guide the main task of predicting the driving commands. Our framework involves an end-to-end tr…
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.
Generative model improves safety in self-driving simulators and human motion generation.
problem Improving generative models for constrained domains like safety-critical applications.
method Developed Gen-neG, a denoising diffusion model that uses oracle-assisted guidance.
result Empirically validated Gen-neG for collision avoidance and safety-guarded human motion generation.
Reinforcement learning controls car speed for safe, efficient, and comfortable driving.
problem Safe, efficient, and comfortable car following during autonomous driving.
method Deep reinforcement learning with a reward function for safety, efficiency, and comfort.
result The model reduces dangerous minimum time to collision to 8% of human drivers and maintains efficient headways.
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…
This paper aims to promote real-world use of DRL in autonomous driving.
problem Challenges in deploying DRL in real-world autonomous driving systems.
method Overview of AD tasks, RL algorithms, and DRL applications; discussion of deployment challenges.
result Challenges to real-world deployment of DRL in AD systems.
End-to-end learnable network for safer self-driving with interpretable intermediate representations.
problem Safe motion planning for self-driving vehicles.
method Differentiable semantic occupancy representation for cost calculation in motion planning.
result Significantly outperforms state-of-the-art planners in imitating human behaviors and producing safer trajectories.
Simple physical modifications can fool autonomous driving systems.
problem Vulnerability of autonomous driving models to adversarial attacks.
method Demonstrated end-to-end attacks on autonomous driving using simple physical modifications.
result Simple physical modifications can induce activation patterns similar to different scenarios used in training, fooling autonomous driving models.
A new RL method boosts exploration efficiency in Atari games and driving simulators.
problem Efficient exploration in reinforcement learning environments.
method A two-component approach: decaying schedule and upper quantile bonus.
result Significantly outperforms QR-DQN in Atari games and near-optimal safety in CARLA.
Deep Q-learning analyzes EEG for drowsiness during driving tests.
problem Estimating drowsiness during driving to improve safety.
method Adapting Q-learning to EEG data for drowsiness estimation.
result Trained model accurately tracks mind state variations in EEG data.
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.
The paper uses deep reinforcement learning to control autonomous lane changes safely.
problem Safe and efficient autonomous lane changes in vehicles.
method Deep Q-networks and quadratic approximators for decision-making and control.
result Demonstrated effectiveness in simulations for decision-making and control.
DRIVE improves IV estimation by accounting for distributional uncertainties.
problem Challenges in IV estimation due to untestable model assumptions and poor finite sample properties.
method DRIVE is a distributionally robust IV estimation method that minimizes a square root TSLS objective with a Wasserstein ambiguity set.
result DRIVE achieves consistency without requiring regularization parameter to vanish, ensuring robustness to distributional uncertainties.
Study combines A* and DNN for better pathfinding.
problem Limited applicability of A* in domains without good heuristics.
method Trains a DNN to represent heuristic for A* and integrates it.
result Significantly better performance in driving simulation.
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.
SECRM-2D improves RL-based autonomous driving with safety guarantees.
problem Safety and efficiency trade-offs in RL-based autonomous driving.
method RL-based controller with safety constraints for efficient and comfortable driving.
result SECRM-2D avoids crashes and improves efficiency and comfort compared to baselines.
Paper proposes a method to improve semantic segmentation for fisheye urban driving images.
problem Semantic segmentation for fisheye urban driving images is challenging due to distortion and lack of large datasets.
method A seven degrees of freedom augmentation method is proposed to transform rectilinear images into fisheye images.
result Training with seven-DoF augmentation improves model accuracy and robustness against distorted fisheye data.
Graph neural network predicts vehicle interactions and trajectories for autonomous driving.
problem Predicting future motion of vehicles in traffic scenes.
method Graph neural network that jointly predicts interaction modes and 5-second future trajectories.
result Jointly predicting trajectories and interaction modes leads to lower trajectory error.
The paper evaluates Bayesian neural networks for safety in autonomous driving.
problem Safety guarantees for deep neural network controllers in autonomous driving.
method Developed a framework using a state-of-the-art simulator to evaluate Bayesian controllers.
result Bayesian inference methods can provide statistical guarantees for uncertainty computation in autonomous driving.
Bayesian approach improves car-following model calibration and validation.
problem Insufficient data and computational constraints limit accurate model calibration.
method Bayesian machine learning and probabilistic programming.
result Unique parameter sets estimated for each driver, outperforming standard approaches.
Convolutional neural networks are commonly used to control the steering angle for autonomous cars. Most of the time, multiple long range cameras are used to generate lateral failure cases. In this paper we present a novel model to generate this data and label augmentation using only one short range fisheye camera. We p…
FunCLBM clusters time series data for autonomous driving validation.
problem Validation of autonomous driving systems using large amounts of data.
method FunCLBM model for co-clustering high-dimensional time series data.
result FunCLBM provides structured partition and clustering views of datasets.
New approach uses dynamic programming to efficiently discover failures in autonomous vehicle simulations.
problem Efficiently discovering rare failure events in autonomous vehicle simulations.
method Approximate dynamic programming and scene decomposition to estimate failure distribution.
result Increased number of failures discovered compared to baseline approaches.
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.
Proposes Deep Scenes for interaction-aware scene understanding in reinforcement learning for autonomous driving.
problem Leveraging deep reinforcement learning for high-level decision making in autonomous driving requires handling variable-length sequences of different object types and interactions.
method Introduces Deep Scenes architecture, an extension of Deep Sets or Graph Convolutional Networks, to learn complex interaction-aware scene representations.
result Graph-Q and DeepScene-Q algorithms outperform state-of-the-art methods in evaluations with SUMO.
End-to-end autonomous driving models are vulnerable to simple physical manipulations of images.
problem Vulnerability of end-to-end autonomous driving models to subtle adversarial manipulations of images.
method Developed novel end-to-end attacks using simple physical manipulations (painting black lines on the road) and used Bayesian Optimization to efficiently search for successful attacks.
result Simple physical manipulations can cause autonomous driving models to follow unintended paths, highlighting the vulnerability of these models.
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, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, drive…
In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to …
Unified HS and related methods with explicit modeling assumptions.
problem Lack of clear assumptions in HS methods for Value-at-Risk.
method Explicitly defined parametric model for asset returns and extraction of innovation process.
result HS and related methods require more assumptions than commonly acknowledged.
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.