A Safe and Efficient Real-Time Motion Planning Framework for Autonomous Driving Systems using Model Predictive Path Integral Approach
This paper presents a real-time and safe motion planning framework for autonomous driving systems using the Model Predictive Path Integral (MPPI) approach. The proposed method can handle obstacles and guarantee bounds for speed, acceleration, and steering rate to generate feasible and collision-free trajectories.
Unveiling the Bias in Pedestrian Detection Systems: Fairness Assessment
Current pedestrian detectors exhibit significant bias towards children, highlighting the need for fairness improvements.
SurroundSDF: Implicit 3D Scene Understanding Based on Signed Distance Field
Proposing SurroundSDF for accurate and continuous 3D perception using Signed Distance Fields.
EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding
Proposing EMIE-MAP for accurate large-scale road surface reconstruction using explicit mesh and implicit encoding.
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction
TrajectoryNAS introduces a pioneering method utilizing point cloud data for trajectory prediction, enhancing the performance of autonomous driving systems.
EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding
Proposing EMIE-MAP for large-scale road surface reconstruction using explicit mesh and implicit encoding.
TrajectoryNAS: Enhancing Autonomous Driving with Neural Architecture Search
TrajectoryNAS automates the design of trajectory prediction models using Neural Architecture Search, improving accuracy and efficiency in autonomous driving systems.
EAN-MapNet: Efficient Vectorized HD Map Construction with Anchor Neighborhoods
高精度（HD）マップの効率的な構築方法を提案する。
Safe and Generalized End-to-End Autonomous Driving System with Reinforcement Learning and Demonstrations
Introducing SGADS to enhance safety, generalization, and training efficiency in autonomous driving.
DyRoNet: A Low-Rank Adapter Enhanced Dynamic Routing Network for Streaming Perception
The author introduces DyRoNet, a framework that utilizes low-rank dynamic routing to enhance streaming perception in autonomous driving systems. By integrating specialized pre-trained branch networks and a speed router module, DyRoNet achieves a balance between latency and precision.
