Simulation environments and synthetic data generation for autonomous driving — CARLA, nuPlan, Waymax, IDSim and closed-loop evaluation methodologies.
An autonomous driving research paper: A review of intelligent chassis structure design for electric vehicles with centralized drive configurations.
Engineering 5.0 · Research 7.0 · Business 5.0
To address this issue, we propose SeparateFusion, a novel multisensor fusion framework that integrates four-dimensional (4D) millimeter-wave radar and LiDAR data via a deep neural network.
Engineering 5.0 · Research 8.0 · Business 5.0
To address this challenge, we propose a novel collaborative (CO-) interaction-aware (-IN) MARL framework, named COIN.
Engineering 7.5 · Research 8.0 · Business 5.5
To bridge this gap, we introduce SkyShield, to the best of our knowledge the first front-view monocular semantic occupancy benchmark for urban UAV flight below 20 meters.
Engineering 5.0 · Research 7.0 · Business 5.0
This paper proposes a coupled prediction-planning framework that deeply integrates intention-aware multi-agent prediction with gap-driven trajectory optimization.
Engineering 5.5 · Research 8.0 · Business 5.0
Our approach extends two representative backbones: a radar-camera pipeline where radar substitutes LiDAR, and a LiDAR-radar pipeline where radar complements LiDAR.
Engineering 5.0 · Research 7.0 · Business 5.0
To model vehicle-type-specific pedestrian crash avoidance behavior, we develop a Smooth-Mamba Deep Deterministic Policy Gradient framework, termed SMamba-DDPG, which integrates smooth action constraints with efficient temporal representation learning.
Engineering 5.5 · Research 8.0 · Business 6.5
This paper provides an in-depth exploration of a design and implementation of multi-sensor perception system for autonomous vehicle (AV).
Engineering 5.5 · Research 7.0 · Business 6.0
We present MORPH-U, a CARLA-based closed-loop stack that fuses LiDAR/radar/camera with V2X (CAM/DENM) into a Local Dynamic Map (LDM) and triggers Hybrid-A* replanning when validated hazards or map changes affect the planned route.
Engineering 5.0 · Research 7.0 · Business 5.0
Developing systems capable of driving autonomously demands not only robust perception of the surrounding environment but also reliable decision-making under real-world uncertainty.
Engineering 6.0 · Research 7.0 · Business 6.5
To address these challenges, this paper proposes an Improved Driving Risk Field-based Multi-objective Trajectory Optimization (IDRF-MTO) method.
Engineering 5.0 · Research 7.0 · Business 5.0
To address this issue, we propose the BEVDrive-E2E to explore the interpretability of the end-to-end model by using visual abstractions in bird's eye view (BEV).
Engineering 5.5 · Research 8.0 · Business 5.0
To address these challenges, this paper presents an instance-centric benchmark for the 3D panoptic occupancy prediction task.
Engineering 5.0 · Research 7.0 · Business 5.0
An autonomous driving research paper: Weather-aware Autonomous Driving with Road Condition Analysis using CARLA.
Engineering 5.0 · Research 7.0 · Business 5.0
Therefore, we propose a multimodal coarse-to-local transformer (MC2L-Transformer), which is composed of a hierarchical transformer architecture.
Engineering 5.5 · Research 7.0 · Business 5.0
In this work, we design a smart human driving vehicle simulator HDSim which is empowered by cognitively inspired modeling and AI models.
Engineering 5.0 · Research 8.0 · Business 5.0
Large language models (LLMs) exhibit strong semantic reasoning capabilities for autonomous driving decision-making; however, their substantial inference latency poses a critical challenge for real-time closed-loop vehicle control.
Engineering 5.5 · Research 7.5 · Business 5.0
The rapid evolution of autonomous driving has made high-fidelity simulation essential for validating safety-critical algorithms.
Engineering 7.5 · Research 7.0 · Business 5.5
The objective of this project is to build up sophisticated autonomous driving simulation based on CARLA platform and reinforcement learning.
Engineering 5.0 · Research 7.0 · Business 5.0
To address these issues, we propose AppleVLM, an advanced perception and planning-enhanced VLM model for robust end-to-end driving.
Engineering 6.0 · Research 8.0 · Business 6.5