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Found 10 papers, 6 papers with code
Passenger hazard perception based on EEG signals for highly automated driving vehicles
Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems.
FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality
Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs).
SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation
To this end, we explore the sparse representation and review the task design for end-to-end autonomous driving, proposing a new paradigm named SparseDrive.
Neural Radiance Field in Autonomous Driving: A Survey
To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning
Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a strong need for automatic tuning methods.
Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems
We propose to use step-wise exploration in parameter space and theoretically derive the zeroth-order policy gradient.
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety
The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks.
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks, like autonomous driving.
