SOTAVerified

Trajectory Planning

Trajectory planning for industrial robots consists of moving the tool center point from point A to point B while avoiding body collisions over time. Trajectory planning is sometimes referred to as motion planning and erroneously as path planning. Trajectory planning is distinct from path planning in that it is parametrized by time. Essentially trajectory planning encompasses path planning in addition to planning how to move based on velocity, time, and kinematics.

Papers

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Showing 11–20 of 324 papers

TitleStatusHype
World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks—0
Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving—0
TrackVLA: Embodied Visual Tracking in the Wild—0
Attention-Enhanced Prompt Decision Transformers for UAV-Assisted Communications with AoI—0
Articulatory modeling of the S-shaped F2 trajectories observed in Öhman's spectrographic analysis of VCV syllables—0
GaussianFusion: Gaussian-Based Multi-Sensor Fusion for End-to-End Autonomous DrivingCode0
FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving—0
Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling—0
VERDI: VLM-Embedded Reasoning for Autonomous Driving—0
Provably safe and human-like car-following behaviors: Part 1. Analysis of phases and dynamics in standard models—0
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All datasetsnuScenesToolBench

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-P3 (Lidar)Collision-3s1.27—Unverified
2UniADCollision-3s0.71—Unverified
3AD-MLPCollision-3s0.24—Unverified
4VAD-Base [jiang2023vad]Collision-3s0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT4-TOPGUNWin rate86.54—Unverified
2Attention BucketWin rate71.5—Unverified
3GPT4- DFSDTWin rate70.4—Unverified