Development of a New Intelligent Algorithm to Improve Autonomous Car Operation
Autonomous Driving Systems (ADS) are transforming modern transportation by enabling safer, more efficient vehicle operation.
Engineering 5.5 · Research 8.0 · Business 5.5
Path Planning - Autonomous Driving Papers, Code and Research Roadmap
Path planning and motion planning for autonomous vehicles — route planning, trajectory optimization, decision-making and planning in complex urban traffic.
Vector field-based motion planning for autonomous exploration in mining environments
Abstract Underground mining environments are renowned for their vast scale, confined spaces, and limited visibility.
Engineering 5.5 · Research 7.0 · Business 5.5
Safe and Fast Motion Planning for UGV on Unknown Uneven Terrain via Terrain Safety Corridors and CBF Constraints
This paper presents a hierarchical motion planning framework that enables safe and fast navigation of UGV on unknown uneven terrain.
Engineering 5.0 · Research 7.0 · Business 5.0
Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments
This paper proposes an integrated end-to-end framework combining a Cross-Modal Attention Fusion (CMAF) module, a Kalman-Graph Neural Network (K-GNN) dynamic obstacle predictor, and a two-layer Proximal Policy Optimization path planning architecture.
Engineering 6.0 · Research 7.0 · Business 6.0
Motion Planning in Dynamic Environments: A Survey from Classical to Modern Methods
This paper presents a comprehensive survey of 138 works, primarily published between 2015 and 2025, spanning both classical and learning-based approaches.
Engineering 5.0 · Research 7.0 · Business 5.0
Integrating ego-conditioned prediction and gap-driven motion planning for safe autonomous driving
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
A review of learning-based motion planning: toward a data-driven optimal control approach
An autonomous driving research paper: A review of learning-based motion planning: toward a data-driven optimal control approach.
Engineering 5.0 · Research 7.0 · Business 5.0
SparseWorld: Enhancing End-to-End Autonomous Driving via World Models with Sparse Scene Representation
In this paper, we present SparseWorld, a lightweight world model that focuses on predicting only the critical layout of the scene, enabling efficient future forecasting for end-to-end driving systems.
Engineering 7.5 · Research 8.0 · Business 5.0
Motion Planning for Autonomous Vehicles using Optimization over Graphs of Convex Sets
Motion planning for autonomous vehicles requires generating collision-free and dynamically feasible trajectories in complex environments under real-time constraints.
Engineering 5.0 · Research 7.0 · Business 5.0
MORPH-U: Multi-Objective Resilient Motion Planning for V2X-Enabled Autonomous Driving in High-Uncertainty Environments via Simulation
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
Hierarchical Motion Planning for MASSs: A COLREGs-Compliant Framework Integrating Offline Trajectory Optimization and Real-Time Coupled Course-Speed Decision Making
Autonomous path planning and collision avoidance (CA) in complex maritime environments are critical challenges for maritime autonomous surface ships (MASSs).
Engineering 5.0 · Research 8.0 · Business 5.0
Multi-Objective Trajectory Optimization for Autonomous Vehicles Based on an Improved Driving Risk Field
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
DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale
In this paper, we propose an alternative Vision-Geometry-Action (VGA) paradigm that advocates dense 3D geometry as the critical cue for autonomous driving.
Engineering 6.0 · Research 7.0 · Business 5.0
Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world representations.
Engineering 5.5 · Research 8.5 · Business 5.0
AutoMoT: A Unified Vision-Language-Action Model with Asynchronous Mixture-of-Transformers for End-to-End Autonomous Driving
To address these challenges, we propose AutoMoT in this work, an end-to-end AD framework that unifies reasoning and action generation within a single vision-language-action (VLA) model.
Engineering 7.0 · Research 8.0 · Business 5.0
Optimal Motion Planning for Autonomous Robotic Systems: Foundations, Algorithms, and Challenges
Autonomous robotic systems are increasingly deployed across manufacturing, logistics, and healthcare due to their ability to efficiently complete difficult and dangerous tasks.
Engineering 5.0 · Research 8.0 · Business 5.0
AGNI: Adaptive Sensor Fusion and Terrain-Aware Navigation for Autonomous Ground Vehicles
This paper presents the mechanical design, sensor fusion strategy, path planning algorithms, and experimental validation of AGNI in challenging terrains.
Engineering 5.0 · Research 7.0 · Business 5.0
A Hierarchical Trajectory Planning Framework for Autonomous Underwater Vehicles via Spatial-Temporal Alternating Optimization
Autonomous underwater vehicle (AUV) motion planning in complex three-dimensional ocean environments remains challenging due to the simultaneous requirements of obstacle avoidance, dynamic feasibility, and energy efficiency.
Engineering 5.5 · Research 7.0 · Business 5.5
FROST-Drive: Scalable and Efficient End-to-End Driving with a Frozen Vision Encoder
We propose FROST-Drive, a novel E2E architecture designed to preserve and leverage the powerful generalization capabilities of a pretrained vision encoder from a Vision-Language Model (VLM).
Engineering 6.5 · Research 8.0 · Business 6.5
Friction-Robust Autonomous Racing Using Trajectory Optimization Over Multiple Models
In this paper, we introduce a trajectory optimization framework that natively incorporates the complex and nonlinear effects of friction uncertainty into the planning process to improve both the performance and robustness of maneuvering at high accelerations.
Engineering 5.0 · Research 7.0 · Business 5.0