# End-to-End Autonomous Driving

The end-to-end planner ManeuverFlow organizes candidate trajectories into eight motion maneuvers, generates them in one step, and scores and locally refines them with shared future BEV evidence, reaching 90.58 PDMS and 90.14 EPDMS on NAVSIM v1/v2 navtest.

The end-to-end planner ManeuverFlow organizes candidate trajectories into eight motion maneuvers, generates them in one step, and scores and locally refines them with shared future BEV evidence, reaching 90.58 PDMS and 90.14 EPDMS on NAVSIM v1/v2 navtest.

Autonomous-driving vision-language-action models have learned from a scalar trajectory score that flags a bad plan but cannot say whether scene understanding, behavior selection, or trajectory execution went wrong. Reflect-VLA treats reflective guidance, which pairs evaluation diagnoses with corrective suggestions, as training-time privileged information and internalizes it through dual-path on-policy self-distillation: risky trajectories align with the teacher's token-level corrective distribution, while reliable ones are preserved by self-imitation. It reaches competitive planning performance on NAVSIM v1/v2 and Bench2Drive, cuts training time by over 90% versus reinforcement learning, and generates trajectories in a single pass at inference without guidance or iterative refinement.

Autonomous-driving vision-language-action models have learned from a scalar trajectory score that flags a bad plan but cannot say whether scene understanding, behavior selection, or trajectory execution went wrong. Reflect-VLA treats reflective guidance, which pairs evaluation diagnoses with corrective suggestions, as training-time privileged information and internalizes it through dual-path on-policy self-distillation: risky trajectories align with the teacher's token-level corrective distribution, while reliable ones are preserved by self-imitation. It reaches competitive planning performance on NAVSIM v1/v2 and Bench2Drive, cuts training time by over 90% versus reinforcement learning, and generates trajectories in a single pass at inference without guidance or iterative refinement.

The work proposes a dual-process-inspired hybrid framework in which a TransFuser++ neural planner generates human-like trajectories in routine driving while a meta-cognitive component, using risk fields shaped by knowledge-graph reasoning, decides when to switch to a model predictive control planner; tested in CARLA on out-of-distribution scenarios of vehicles (including an ambulance) running a red light, it reduced vehicle-to-vehicle collisions from 48 to 5 out of 160 simulations, an 89% reduction.

The work proposes a dual-process-inspired hybrid framework in which a TransFuser++ neural planner generates human-like trajectories in routine driving while a meta-cognitive component, using risk fields shaped by knowledge-graph reasoning, decides when to switch to a model predictive control planner; tested in CARLA on out-of-distribution scenarios of vehicles (including an ambulance) running a red light, it reduced vehicle-to-vehicle collisions from 48 to 5 out of 160 simulations, an 89% reduction.

The authors introduce ViRA, a planner-agnostic visual representation alignment framework that supervises the planner encoder with frozen visual foundation model (VFM) intermediate features during training and discards the alignment modules at inference, leaving planner architecture and inference cost unchanged; across NAVSIM v2 and HUGSIM, ViRA consistently improves driving performance for regression-based, diffusion-based, and scoring-based planners, with gains depending on the VFM target and on whether the planner uses auxiliary perception supervision.

The authors introduce ViRA, a planner-agnostic visual representation alignment framework that supervises the planner encoder with frozen visual foundation model (VFM) intermediate features during training and discards the alignment modules at inference, leaving planner architecture and inference cost unchanged; across NAVSIM v2 and HUGSIM, ViRA consistently improves driving performance for regression-based, diffusion-based, and scoring-based planners, with gains depending on the VFM target and on whether the planner uses auxiliary perception supervision.

The work proposes GeoCoTDrive, an explicit geometric chain-of-thought framework for vision-language-action driving: it first autoregressively grounds 2D regions tied to ego decisions, then samples localized 3D geometry tokens from a frozen geometric foundation model inside those regions and interleaves them into the autoregressive context for trajectory generation; it also formulates planning-relevant grounding and builds the 146K-annotation PlanningGrounding dataset, improving safety-critical planning metrics on nuScenes, NAVSIM and Bench2Drive.

The work proposes GeoCoTDrive, an explicit geometric chain-of-thought framework for vision-language-action driving: it first autoregressively grounds 2D regions tied to ego decisions, then samples localized 3D geometry tokens from a frozen geometric foundation model inside those regions and interleaves them into the autoregressive context for trajectory generation; it also formulates planning-relevant grounding and builds the 146K-annotation PlanningGrounding dataset, improving safety-critical planning metrics on nuScenes, NAVSIM and Bench2Drive.