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.
Waymo Open Dataset provides a large, diverse, and synchronized LiDAR and camera dataset for autonomous driving research.
problem Limited diversity and scale in existing self-driving datasets hinder real-world problem alignment.
method Developed a new large-scale, high-quality, diverse dataset with synchronized LiDAR and camera data.
result The dataset is 15x more diverse than the largest existing dataset based on a proposed diversity metric.
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.
D2-City is a diverse traffic dataset for AI development.
problem Lack of comprehensive, detailed datasets for AI in driving.
method Collects over 10,000 dashcam videos with detailed annotations.
result Advances AI in driving perception and related areas.
Autonomous driving is getting a lot of attention in the last decade and will be the hot topic at least until the first successful certification of a car with Level 5 autonomy. There are many public datasets in the academic community. However, they are far away from what a robust industrial production system needs. Ther…
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.
Predict steering angles of self-driving cars from images.
problem Predicting steering angles for self-driving cars using image data.
method Used deep learning techniques like Transfer Learning, 3D CNN, LSTM, and ResNet to predict steering angles.
result Both models placed in the top ten of Udacity's challenge.
WoodScape dataset for fisheye cameras in autonomous driving.
problem Lack of public datasets for evaluating computer vision on fisheye images.
method Release WoodScape dataset with four surround view cameras and nine tasks.
result Encourages adaptation of computer vision models for fisheye cameras.
Improved motion prediction for self-driving cars using trajectory sets and auxiliary losses.
problem Accurately predicting future vehicle motion for self-driving cars.
method Classification over trajectory sets with an auxiliary loss for off-road predictions and spatial-temporal relationships.
result Significant improvement in motion prediction performance on small datasets using map information.
SoilingNet detects soiling on automotive cameras for better autonomous driving performance.
problem Soiling degrades the performance of automotive surround-view cameras, affecting autonomous driving.
method Created a new dataset and used a Convolutional Neural Network (CNN) architecture for soiling detection, combined with multi-task learning and data augmentation using GANs.
result Demonstrated high accuracy in soiling detection using CNN and multi-task learning.
With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning algorithms based on SMART attributes are effective methods to predict hard drive failures. In this paper, we use SMART attributes to predict…
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.
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.
Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to …
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.
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…
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.
Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible for its use in automated driving. In this paper, the semantic segmentation problem is explored from the perspective of automated driving. Mos…
Benchmark improves object detection robustness in winter weather.
problem Assessing object detection models' performance under image corruptions.
method Developed three benchmark datasets with various image corruptions; used data augmentation to improve robustness.
result Simple data augmentation significantly enhances model robustness across different corruptions and datasets.
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.
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.
KEMP predicts long-term trajectories for autonomous driving using keyframes.
problem Predicting future trajectories of road agents for autonomous driving.
method Keyframe-based hierarchical end-to-end deep learning framework.
result Ranked 1st on Waymo Open Motion Dataset Leaderboard.
Self-supervised method estimates distances on fisheye cameras for autonomous driving.
problem Accurate Euclidean distance estimation on fisheye cameras for autonomous driving.
method Self-supervised scale-aware framework for monocular fisheye videos.
result State-of-the-art results on KITTI dataset, comparable to other methods.
Adversarial attacks can fool self-driving cars, changing steering predictions.
problem Security of deep neural networks in self-driving cars.
method Demonstrated adversarial attacks on steering angle prediction model.
result Small image modifications can mislead self-driving car predictions.
Paper discovers manoeuvres from vehicle telematics data.
problem Analyzing driving behaviour from vehicle data.
method Used motif detection in time-series with a modified EMD algorithm.
result Validated motif discovery for complex manoeuvres.
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.
Improved vehicle motion prediction with uncertainty estimation.
problem Robust motion prediction for autonomous vehicles, especially under distributional shift.
method Presented an approach significantly improving the benchmark and taking 2nd place on the leaderboard.
result Significantly improved motion prediction and uncertainty measurement.
Improved road segmentation on low-res LIDAR data for autonomous vehicles.
problem Low-resolution LIDAR data affects road segmentation accuracy in autonomous vehicles.
method Subsampled LIDAR data transformation into feature maps, use local normal vector with spherical coordinates.
result Improves road segmentation accuracy on low-resolution LIDAR data.
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…
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.
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them ca…
Doubly robust self-training improves semi-supervised learning by balancing labeled and pseudo-labeled data.
problem Improving semi-supervised learning performance with limited labeled data.
method Introduces doubly robust self-training, a method that combines labeled and pseudo-labeled data to balance between labeled-only and pseudo-labeled-only training.
result Demonstrates superior performance of doubly robust self-training on ImageNet and nuScenes datasets.
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.
Develops methods to improve reliability of deep learning for autonomous driving.
problem Safety concerns in deploying autonomous driving systems.
method Introduces a new criterion (true class probability) for estimating model confidence and learns it from data.
result Proposed method provides better failure prediction than current uncertainty measures.
Paper tackles drowsy driving by learning from weakly labeled car acceleration data.
problem Lack of labeled data for estimating driver drowsiness.
method Weakly supervised learning, scalable stochastic optimization.
result Algorithm learns from weakly labeled data, outperforming baseline methods.
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.
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.
Deep neural features identify unique vehicles from dash-cam feeds.
problem Identifying unique vehicles in dash-cam feeds for self-driving cars.
method Used pretrained YOLO network feature maps to create deep integrated feature signatures (DIFS) for 700 images of 35 vehicles and 340 images of 17 vehicles.
result Correctly identified unique vehicles at 96.7% for high resolution data and 86.8% for lower resolution data.
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…
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 rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and widely available datasets, has seen them being increasingly used within safety-critical domains. One such domain, self-driving, has benefited from significant performance improvements, with millions of miles having been driven wit…
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 …
PAC-Wrap provides provable guarantees for semi-supervised anomaly detection.
problem Ensuring reliable anomaly detection in safety-critical applications.
method PAC-Wrap wraps around existing anomaly detection methods to provide PAC guarantees.
result PAC-Wrap effectively provides rigorous guarantees for various anomaly detectors.
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.
Classifying human cognitive states from behavioral and physiological signals is a challenging problem with important applications in robotics. The problem is challenging due to the data variability among individual users, and sensor artefacts. In this work, we propose an end-to-end framework for real-time cognitive wor…
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.
Shifts dataset evaluates uncertainty in real-world tasks across modalities.
problem Lack of standard datasets for evaluating uncertainty estimation and robustness to distributional shift.
method Proposes Shifts Dataset for evaluation of uncertainty estimates and robustness to distributional shift across tabular, audio, text, and sensor data.
result Baseline results for tabular weather prediction, machine translation, and SDC vehicle motion prediction.
This research uses PointNets to detect 2D objects from radar data.
problem Detecting 2D objects from sparse radar data for automated driving.
method Adapting PointNets for radar data, performing 2D object classification and bounding box regression.
result Demonstrates the potential of PointNets for 2D object detection in radar data.