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Found 16 papers, 10 papers with code
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
Current methods of imitation learning (IL), primarily based on deep neural networks, offer efficient means for obtaining driving policies from real-world data but suffer from significant limitations in interpretability and generalizability.
Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An Inductive Logic Programming Approach
This approach is more robust against adversarial attacks, as it mimics human-like perception and is less susceptible to the limitations of current DNN classifiers.
Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving.
Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays
By adjusting controller parameters in real-time, this adaptive controller compensates for stochastic delays and improves synchronicity between local and remote robotic manipulators.
Decision Making for Autonomous Driving in Interactive Merge Scenarios via Learning-based Prediction
This paper presents a decision making approach for autonomous driving, focusing on the complex task of merging into moving traffic where uncertainty emanates from the behavior of other drivers and imperfect sensor measurements.
Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning
This paper proposes a novel scoring function for the planning module of MPC-based reinforcement learning methods to address the inherent bias of using the reward function to score trajectories.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks
Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records.
ARC: Adversarially Robust Control Policies for Autonomous Vehicles
By training the protagonist against an ensemble of adversaries, it learns a significantly more robust control policy, which generalises to a variety of adversarial strategies.
Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data
By penalising the safe action distribution based on its similarity to the unsafe action distribution when training on the collision dataset, a more robust and safe control policy is obtained.
Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments
Terrain traversability analysis plays a major role in ensuring safe robotic navigation in unstructured environments.
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments
Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-board power.
Self-adaptive Torque Vectoring Controller Using Reinforcement Learning
It is shown that, torque-vectoring controller with parameter tuning via reinforcement learning performs well on a range of different driving environment e. g., wide range of friction conditions and different velocities, which highlight the advantages of reinforcement learning as an adaptive algorithm for parameter tuning.
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
We compare models with and without safety cages, as well as models with optimal and constrained model parameters, and show that the weak supervision consistently improves the safety of exploration, speed of convergence, and model performance.
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
As the networks used to obtain state-of-the-art results become increasingly deep and complex, the rules they have learned and how they operate become more challenging to understand.
A Survey of Deep Learning Applications to Autonomous Vehicle Control
However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios.
Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors
In contrast, the late fusion scheme fuses the independently detected bounding boxes from multiple spatially diverse sensors.
