Self-driving vehicles improve safety by predicting surrounding vehicles' trajectories.
problem Ensuring safety of self-driving vehicles through better trajectory prediction.
method Developed a Convolutional Neural Network to forecast vehicle trajectories from raw data.
result Improvement over baseline models in trajectory forecasting accuracy.
Paper classifies pedestrians and vehicles detected by LiDAR.
problem Classifying objects from LiDAR data for self-driving cars.
method Used LiDAR-based object detector and Neural Networks classifier.
result Real-time object detection for self-driving vehicles.
This work improves vehicle trajectory prediction for safer self-driving cars.
problem Improving trajectory predictions for safer self-driving cars.
method Deep-learning-based method combining AI and physically grounded models.
result Proposes a method that outperforms existing state-of-the-art.
Project accelerates pedestrian detection in self-driving cars using FPGA.
problem Improve pedestrian detection accuracy in self-driving vehicles.
method Training and tuning SSD, FPGA design for acceleration.
result 4x performance improvement with minimal accuracy loss.
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.
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.
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.
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.
The paper analyzes vehicle encounters using driving primitives.
problem Understanding complex vehicle encounters for autonomous driving.
method Decompose driving data into primitives using nonparametric Bayesian learning.
result More than 4000 driving primitives identified from 976 encounters.
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.
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.
Paper assesses uncertainty in safety evaluation of self-driving tech.
problem Estimating uncertainty in safety-critical event probabilities.
method Combines bootstrap method and likelihood ratio scheme for input uncertainty assessment.
result Demonstrates the impact of data variability on safety evaluation estimates.
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.
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.
VTrackIt creates a synthetic dataset with infrastructure and vehicle info for AVs.
problem Lack of infrastructure and pooled vehicle info in existing AV datasets.
method Developed VTrackIt, a synthetic dataset with intelligent infrastructure and pooled vehicle info, and introduced InfraGAN for trajectory predictions.
result VTrackIt reduces high-risk edge cases in AV trajectory predictions.
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.
Proposes a privacy-preserving system for federated learning of road networks.
problem Privacy and security of data shared between vehicles and infrastructure.
method Federated learning over V2V and V2N links, non-IID dataset modeling.
result Improves learning performance and prevents eavesdropping.
Deep learning predicts traffic actors' future movements with uncertainty.
problem Predicting future states of traffic actors for autonomous vehicles.
method Deep learning models using raster images of actors' surroundings.
result Effective prediction of traffic actors' movements with uncertainty.
Safe control for vehicles using learned perception from images.
problem Controlling autonomous vehicles with partial state information from images.
method Learned perception map and safe set design for a closed loop system.
result Generalization properties of the perception-control loop are favorable.
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.
Paper proposes incorporating road rules as a loss function for better motion planning.
problem Lack of structured priors in perception and motion forecasting methods.
method Integrates road rules as a loss function in a probabilistic model using REINFORCE.
result Motion forecasts result in safer plans for self-driving vehicles.
Self-driving technology is advancing rapidly --- albeit with significant challenges and limitations. This progress is largely due to recent developments in deep learning algorithms. To date, however, there has been no systematic comparison of how different deep learning architectures perform at such tasks, or an attemp…
Even though end-to-end supervised learning has shown promising results for sensorimotor control of self-driving cars, its performance is greatly affected by the weather conditions under which it was trained, showing poor generalization to unseen conditions. In this paper, we show how knowledge can be transferred using …
VectorNet predicts car behavior using vectorized HD maps and agent dynamics.
problem Predicting behavior in multi-agent systems with self-driving cars.
method VectorNet uses hierarchical graph neural networks on vectorized representations of HD maps and agent trajectories.
result VectorNet achieves comparable or better performance than state-of-the-art methods while using fewer parameters and less computational power.
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.
Tackles bridging machine learning and control theory for safety-critical systems.
problem Ensuring reliability and safety in machine learning applications for safety-critical systems.
method Review of recent advances in learning and control theory, historical context.
result Importance of control theorists joining the conversation on learning-related problems.
Framework fuses RGB images and depth maps for self-driving car control.
problem Fault tolerance in self-driving cars with sensor failures.
method Deep neural network architecture for sensor fusion.
result Framework can learn to use relevant sensor information even when one fails.
Deep CNNs predict multiple actor trajectories for safer autonomous driving.
problem Uncertainty and variety of traffic behaviors in autonomous driving.
method Convert actor surroundings into images, feed into deep CNNs.
result Successfully tested on SDVs in closed-course tests.
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.
The paper evaluates uncertainty measures for self-driving cars to predict crashes.
problem Lack of safety guarantees in DNNs used in self-driving cars.
method Real-time extraction and evaluation of uncertainty from DNNs without altering their architecture.
result Mutual information is a promising indicator of crashes in self-driving cars.
New security patch for object tracking in self-driving cars mitigates vulnerabilities.
problem Vulnerability of object tracking in self-driving cars, especially Kalman Filter-based trackers.
method Adaptive strategy to balance focus on observations and predictions based on deviation anomaly index.
result Certified effectiveness against a generalized hijacking attack model.
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is …
Paper proposes a method to reduce annotation time for 3D object detection.
problem Effort and time required for generating 3D object annotations.
method Combines human supervision with pretrained neural networks for 3D point cloud segmentation and bounding box generation.
result Reduces human annotation time by 30x.
Single network with segmented training prevents forgetting in dynamic systems.
problem Catastrophic forgetting in continual learning for dynamic systems.
method Single Net with Progressive Segmented Training (PST). Model parameters are divided into frozen and secondary groups.
result Achieves state-of-the-art accuracy in single-head evaluation on CIFAR-10 and CIFAR-100 datasets.
In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road. The goal of this paper is to add to the equation two additional crucial parameters. The first is standardization of safety assurance --- wh…
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.
Deep learning tool classifies urban delivery vehicles.
problem Counting and categorizing delivery vehicles in cities.
method Developed annotated database and retrained CNNs.
result Accurate classification of 90%+ for 3 vehicle classes.
Deep neural network detects driver intentions from video.
problem Detecting driver intentions for safer self-driving.
method Uses deep learning to analyze turn signals and emergency flashers.
result High per-frame accuracy in challenging scenarios.
Enhances KWS in vehicles with multi-source fusion.
problem Improving precision and recall rates in vehicle keyword spotting.
method Integrates vehicle information into a DNN for speech classification and selects optimal sensitivity parameters.
result Significantly improved performance metrics (precision, recall, MSE) compared to baseline.
Rank-based Bayesian Optimization improves molecule selection in chemical systems.
problem Optimizing chemical compounds using traditional regression models.
method Introducing Rank-based Bayesian Optimization (RBO) using ranking models.
result RBO outperforms regression-based BO, especially for rough landscapes and activity cliffs.
Adaptive framework generates challenging adversarial scenarios for autonomous vehicles.
problem Lack of efficient and adaptable evaluation methods for autonomous vehicles.
method Adaptive evaluation framework using ensemble models and nonparametric Bayesian clustering.
result Adversarial scenarios significantly degrade tested autonomous vehicles' performance.
This paper improves traffic actor movement predictions using GANs and a differentiable rasterizer.
problem Predicting future movements of traffic actors for safe autonomous driving.
method A novel raster-based conditional GAN architecture with a differentiable rasterizer.
result The proposed method outperforms state-of-the-art GAN-based baselines.
Generative model predicts vehicle faults up to 1000 hours in advance.
problem Forecasting vehicle faults for predictive maintenance.
method Generative model trained on US Army data, incorporating real-world factors.
result Highly accurate predictions of time to first fault.
Deep learning models control vehicle dynamics on a track.
problem Coupled longitudinal and lateral control of a vehicle.
method Trained two neural networks (MLP and CNN) to compute controls based on high-fidelity simulations.
result Deep learning models outperform conventional controllers on a challenging track.
Generative model learns vehicle trajectory distributions for better data generalization.
problem Data sparsity and privacy issues in urban vehicle trajectory analysis.
method Generative adversarial imitation learning framework for urban vehicle trajectory generation.
result TrajGAIL model produces synthetic trajectories similar to real ones, achieving significant performance gains.
Adaptive stress testing for autonomous vehicles identifies failure scenarios using reinforcement learning.
problem Identifying potential failure scenarios in autonomous vehicle decision-making systems.
method Formulated as a Markov decision process, used reinforcement learning (DRL) to find likely failure scenarios.
result Deep Reinforcement Learning (DRL) finds more likely failure scenarios with fewer simulator calls than Monte Carlo Tree Search (MCTS).
This paper studies users' perception regarding a controversial product, namely self-driving (autonomous) cars. To find people's opinion regarding this new technology, we used an annotated Twitter dataset, and extracted the topics in positive and negative tweets using an unsupervised, probabilistic model known as topic …
Proposes a method to model multi-vehicle interactions using Gaussian processes.
problem Challenges in modeling correlations between multiple road users over time.
method Uses a stochastic vector field model and non-parametric Bayesian learning.
result Captures motion patterns from complex multi-vehicle interactions without heroic prior assumptions.