AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2601.01762v1
• Date: Mon, 05 Jan 2026 03:41:20 GMT
• Title: AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
• Abstract summary: In the planning stage, state-of-the-art autonomous driving models decouple planning into parallel lateral and longitudinal predictions.<n>We propose a novel cascaded framework that explicitly conditions longitudinal planning on the drive path.<n>Our method sets a new SOTA, achieving a driving score of 89.07 and a success rate of 73.18%, demonstrating significantly improved coordination and safety.
• Score: 23.843979782864427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end autonomous driving has rapidly progressed, enabling joint perception and planning in complex environments. In the planning stage, state-of-the-art (SOTA) end-to-end autonomous driving models decouple planning into parallel lateral and longitudinal predictions. While effective, this parallel design can lead to i) coordination failures between the planned path and speed, and ii) underutilization of the drive path as a prior for longitudinal planning, thus redundantly encoding static information. To address this, we propose a novel cascaded framework that explicitly conditions longitudinal planning on the drive path, enabling coordinated and collision-aware lateral and longitudinal planning. Specifically, we introduce a path-conditioned formulation that explicitly incorporates the drive path into longitudinal planning. Building on this, the model predicts longitudinal displacements along the drive path rather than full 2D trajectory waypoints. This design simplifies longitudinal reasoning and more tightly couples it with lateral planning. Additionally, we introduce a planning-oriented data augmentation strategy that simulates rare safety-critical events, such as vehicle cut-ins, by adding agents and relabeling longitudinal targets to avoid collision. Evaluated on the challenging Bench2Drive benchmark, our method sets a new SOTA, achieving a driving score of 89.07 and a success rate of 73.18%, demonstrating significantly improved coordination and safety

Related papers

• MomADv2: Reliable Temporal Memory for End-to-End Autonomous Driving [37.331403884871136]
  MomADv2 is a reliable state-space memory framework for end-to-end autonomous driving.<n>It filters historical planning queries based on temporal continuity and command consistency.<n>It learns a continuous residual correction field from the refined planning output to the expert trajectory.
  arXiv  Detail & Related papers  (2026-08-24T15:51:07Z)
• GraphWorld: Long-Horizon Planning with World Models for End-to-End Autonomous Driving [56.50032398571235]
  GraphWorld is an E2E-AD framework that explicitly enhances long-horizon planning through latent world modeling.<n>We present a World-State-Conditioned Planning that learns ego-centric latent world representations by modeling interactions between an ego vehicle and surrounding agents.<n>This latent world state captures key interaction dynamics and safety-relevant semantics, and serves as a conditioning signal to guide long-horizon, safety-aware trajectory planning.
  arXiv  Detail & Related papers  (2026-06-15T06:26:46Z)
• PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models [11.0972429896041]
  We propose PLAN-S (PLANning with latent Style dynamics), a planner-facing bridge that addresses the compactness-controllability dilemma.<n>PLAN-S produces diverse cost maps, with spatially consistent variations aligned to different driving styles.
  arXiv  Detail & Related papers  (2026-06-04T11:03:20Z)
• Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling [74.41886258801209]
  We propose a two-stage trajectory planning framework that decouples principle alignment from behavior learning.<n>Plan-R1 significantly improves planning safety and feasibility, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-05-23T09:22:19Z)
• DiFSD: Ego-Centric Fully Sparse Paradigm with Uncertainty Denoising and Iterative Refinement for Efficient End-to-End Self-Driving [55.53171248839489]
  We propose an ego-centric fully sparse paradigm, named DiFSD, for end-to-end self-driving.<n>Specifically, DiFSD mainly consists of sparse perception, hierarchical interaction and iterative motion planner.<n>Experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superior planning performance and great efficiency of DiFSD.
  arXiv  Detail & Related papers  (2024-09-15T15:55:24Z)
• Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures [13.648498083189931]
  Perception Helps Planning (PHP) reconciles lane-level planning with perception.<n> Experiments on three Carla benchmarks show significant improvements in driving score of 27.20%, 33.47%, and 15.54% over existing algorithms.
  arXiv  Detail & Related papers  (2024-07-16T12:12:06Z)
• SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation [11.011219709863875]
  We propose a new end-to-end autonomous driving paradigm named SparseDrive. SparseDrive consists of a symmetric sparse perception module and a parallel motion planner. For motion prediction and planning, we review the great similarity between these two tasks, leading to a parallel design for motion planner.
  arXiv  Detail & Related papers  (2024-05-30T02:13:56Z)
• An Online Spatial-Temporal Graph Trajectory Planner for Autonomous Vehicles [6.907105812732423]
  In this paper, a novel online spatial-temporal graph trajectory planner is introduced to generate safe and comfortable trajectories. To support the planner, a simple behavioral layer is also presented that determines kinematic constraints for the planner. Results show that the proposed planner generates safe and feasible trajectories while achieving similar or longer distances in the forward direction and comparable comfort ride.
  arXiv  Detail & Related papers  (2024-04-18T15:22:29Z)
• Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving [45.18582668677648]
  Planning the trajectory of the controlled ego vehicle is a key challenge in automated driving. We propose a lightweight equivariant planning model that generates multi-modal joint predictions for all vehicles. We also propose equivariant route attraction to guide the ego vehicle along a high-level route provided by an off-the-shelf GPS navigation system.
  arXiv  Detail & Related papers  (2024-03-17T18:53:46Z)
• Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging scenarios [5.193470362635256]
  Existing methods cannot initiate prediction for a vehicle unless observed for a fixed duration of two or more seconds. This paper proposes a novel transformer-based trajectory prediction approach, specifically trained to handle any observation length larger than one frame. We perform a comprehensive evaluation of the proposed method using two large-scale highway trajectory datasets.
  arXiv  Detail & Related papers  (2023-06-08T18:03:48Z)
• Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset and Consensus-Based Models [76.32775745488073]
  We present a novel dataset and modeling framework designed to study motion planning in understructured environments.<n>We demonstrate that a consensus-based modeling approach can effectively explain the emergence of priority orders observed in our dataset.
  arXiv  Detail & Related papers  (2022-09-19T05:06:57Z)
• Deep Structured Reactive Planning [94.92994828905984]
  We propose a novel data-driven, reactive planning objective for self-driving vehicles. We show that our model outperforms a non-reactive variant in successfully completing highly complex maneuvers.
  arXiv  Detail & Related papers  (2021-01-18T01:43:36Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)
• PiP: Planning-informed Trajectory Prediction for Autonomous Driving [69.41885900996589]
  We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets.
  arXiv  Detail & Related papers  (2020-03-25T16:09:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.