End-to-End Autonomous Driving Systems

• End-to-end autonomous driving systems are unified frameworks that convert raw sensor inputs into control commands, bypassing traditional modular pipelines.
• They integrate advanced neural architectures and multi-modal sensor fusion to jointly optimize perception, planning, and decision-making.
• Innovations such as cooperative V2X planning, diffusion-based planners, and adaptive loss functions boost real-time performance, robustness, and safety.

End-to-end autonomous driving systems refer to machine learning-based frameworks in which raw sensor data are mapped directly to trajectory plans or low-level control commands (e.g., steering, throttle, brake) via a single, fully differentiable model. In contrast to traditional modular pipelines with separate perception, localization, prediction, planning, and control components, end-to-end systems jointly optimize all sub-tasks with the goal of minimizing a global driving objective. This paradigm aims to increase efficiency, reduce annotation and engineering overhead, and potentially improve robustness through unified feature learning.

1. Structural Principles and System Architectures

End-to-end autonomous driving (E2EAD) systems are characterized by integrating the entire perceptual, reasoning, and action pipeline into a single neural computation graph. The foundational mapping is $f_\theta: X \rightarrow Y$, with $X$ encompassing raw sensory streams (multi-view RGB images, LiDAR, radar, GPS/IMU), ego-vehicle status, and optionally auxiliary information such as navigation commands or HD maps, and $Y$ being either a sequence of future trajectory waypoints or direct control commands. Architectures typically comprise a visual backbone (CNN, vision transformer, or BEV encoder), sensor fusion layers if multi-modal inputs are available, sequence modeling (RNN/GRU, Transformer, or SSM), and one or more output heads for planning or control.

Key variants include:

• Unified Transformer Decoders: Architectures such as UniAD unify detection, tracking, prediction, and planning into a single transformer stack with shared queries. UAD pioneers an unsupervised variant, bypassing annotated 3D perception via a BEV-centric, angular sector-based auxiliary task (Guo et al., 2024).
• Multimodal Language-Model Augmented Systems: LLM-driven designs (LeAD, EMMA) fuse sensory features with high-level semantic representations, enabling complex scenario comprehension and explicit chain-of-thought (CoT) reasoning for edge cases (Zhang et al., 8 Jul 2025, Hwang et al., 2024).
• Sparse and Instance-Centric Representations: SparseDrive organizes the entire scene as sets of agent and map-element instances, using deformable attention to achieve both perception and planning in a computationally efficient, instance-level graph (Sun et al., 2024).
• Diffusion-Based Planners: Systems like TrajDiff cast planning as a generative diffusion process over trajectory space, conditioning only on self-supervised BEV "driving probability" heatmaps, thereby eliminating dependence on any perception annotations (Gui et al., 30 Nov 2025).
• DRL with BEV Grounding and State-Space Modeling: ME$^3$-BEV leverages a Lift-Splat BEV encoder integrated with MAMBA/TAFM–based state-space sequence modeling, embedding structured spatio-temporal knowledge for deep reinforcement learning (Lu et al., 8 Aug 2025).

2. Learning Paradigms, Losses, and Supervision Strategies

End-to-end frameworks predominantly employ three learning paradigms:

• Imitation Learning (IL): Behavioral cloning remains foundational, minimizing $$L_{\text{IL}}(\theta) = \mathbb{E}_{(x,a^*)}[\|f_\theta(x) - a^*\|^2]$$ over expert-labeled sensor-action pairs. Extensions include directional augmentation (trajectory rotation), self-supervised consistency (e.g., UAD's $\mathcal{L}_{\text{cons}}$), and domain balancing (Guo et al., 2024).
• Reinforcement Learning (RL): Policy optimization directly maximizes accrued driving rewards, e.g., via PPO with learned BEV features (Lu et al., 8 Aug 2025). Hybrid paradigms combine IL and RL terms to gather robustness while maintaining sample efficiency (Chen et al., 2023).
• Self-/Unsupervised and Proxy Supervision: To reduce annotation costs, UAD and TrajDiff introduce proxy objectives—angular objectness, trajectory-centric BEV heatmap matching, and diffusion denoising losses—trained end-to-end using only 2D detectors or expert trajectory logs, thus circumventing dependence on labeled 3D perception (Guo et al., 2024, Gui et al., 30 Nov 2025).

Auxiliary losses frequently target spatial and temporal consistency (e.g., UAD's $\mathcal{L}_{\text{drm}}$ KL dreaming loss), direction classification, and—where applicable—safety or comfort metrics (barrier-function losses, collision penalties). Multi-task objectives balance these, sometimes adaptively via learnable weights or scenario-aware gating (Yin et al., 17 Sep 2025).

3. Multimodal, Cooperative, and Semantic-Enhanced Designs

Contemporary E2EAD research emphasizes the integration of multiple sensing modalities and external knowledge sources:

• Sensor Fusion: Approaches include early (input concatenation), mid-level (joint feature computation via cross-attention), and late (independent output merging) fusion, supporting configurations such as LiDAR-as-camera (Tampuu et al., 2022), hybrid camera–LiDAR BEV-based planning (Lu et al., 8 Aug 2025), or multimodal transformers (Gemini, Florence-2) (Hwang et al., 2024, You et al., 2024).
• V2X Cooperative Planning: V2X-VLM and UniE2EV2X establish fully end-to-end, multi-agent architectures where vehicle and infrastructure sensors' features are jointly fused in BEV or vision-language embedding space, often with cross-attention or deformable attention modules, to bolster occlusion robustness and accident prediction capability (You et al., 2024, Li et al., 2024).
• Semantic and Map Integration: MAP and related kindred models fuse online-constructed semantic maps (e.g., Panoptic BEV segmentations) into planning via dynamic query attention and ego-status gating, reducing off-road and collision errors without heavy explicit tracking modules (Yin et al., 17 Sep 2025).
• Foundation Model Augmentation: Drive Anywhere, EMMA, and LeAD employ large pre-trained multi-modal models for robust open-set generalization, counterfactual debugging, and chain-of-thought–driven high-level actions. Latent space language-driven augmentation and explainability tools are emerging hallmarks of this line (Wang et al., 2023, Hwang et al., 2024, Zhang et al., 8 Jul 2025).

4. Evaluation Methodologies and Performance Metrics

Evaluation protocols for E2EAD comprise open-loop and closed-loop assessments:

• Open-Loop (Offline) Metrics: L2 displacement error, average collision rate, and trajectory-based metrics (minADE/minFDE, miss rate) are standard. For instance, UAD achieves Avg L2=0.45 m and avg collision=0.06% on nuScenes (TemAvg), surpassing supervision-intensive baselines (Guo et al., 2024). Planning-specific scores such as PDMS (Predictive Driving Model Score) aggregate safety, drivable-area compliance, progress, comfort, and collision penalties (Gui et al., 30 Nov 2025).
• Closed-Loop Simulation/Real-World: Metrics encompass Route Completion (RC), Infraction Score (IS), Driving Score (DS), collision/off-road rates, and comfort indices (jerk, smoothness). CARLA Leaderboard, nuScenes, and DAIR-V2X benchmarking platforms provide standard evaluation environments, often with protocol variations to assess generalization (unseen cities, weather) (Lan et al., 2023, You et al., 2024).
• Real-Time & Resource Efficiency: Inference speed, training time, and resource consumption are critical, especially for deployment. SparseDrive and UAD notably deliver order-of-magnitude improvements in resource utilization compared to modular or dense BEV-centric stacks (Sun et al., 2024, Guo et al., 2024).

Empirical results from leading models:

Model	Open-loop L2 (nuScenes, m)	Collision Rate (%)	Driving Score (CARLA)	Inference FPS
UAD	0.45	0.06	71.63	7.2
UniAD	0.69	0.12	NA	2.1
SparseDrive-B	0.58	0.06	NA	7.3
V2X-VLM	1.22 (DAIR-V2X)	0.01	NA	35+
LeAD	NA	NA	71 (CARLA DS)	NA

5. Interpretability, Robustness, and Safety

One of the principal criticisms of E2EAD is limited interpretability relative to modular stacks; recent solutions incorporate:

• Visual and Semantic Attention: Attention overlays (Grad-CAM, auxiliary depth/segmentation heads), saliency maps, and explicit attention alignment between model and human gaze are used for diagnosis and transparency (Aoki et al., 2023, Duan et al., 2024).
• Counterfactual and Language Querying: Foundation model–based systems enable patch-wise semantic manipulation—changing spatial features to “pedestrian” or “roadblock”—and querying via natural language, enabling scenario debugging and testing (Wang et al., 2023).
• Uncertainty and Safety Filters: Some approaches integrate explicit collision checking, safety mesh layers, and confidence-based gating between planners and high-level reasoners (see LeAD, MAP, UAD) (Zhang et al., 8 Jul 2025, Numan et al., 2024, Guo et al., 2024). Empirically, collision rates in state-of-the-art models are now below 0.1% under open-loop or short closed-loop evaluations.
• Human-Guided Augmentation: Integration of human behavioral cues (eye-tracking, intention via EEG) as additional supervision has been shown to marginally but consistently improve driving scores and robustness, with particular benefit when attention alignment losses are included (Duan et al., 2024).

6. Challenges, Limitations, and Emerging Directions

E2EAD research confronts structural, technical, and deployment obstacles:

• Labeled Data Bottlenecks: Manual 3D annotation remains an expensive impediment. Methods such as UAD and TrajDiff eliminate 3D supervision entirely, relying only on off-the-shelf 2D detectors or trajectory logs, enabling rapid data scaling and annotation cost reduction (Guo et al., 2024, Gui et al., 30 Nov 2025).
• Generalization and Edge Cases: Robustness to out-of-distribution scenarios—novel weather, complex urban layouts, and infrequent object interactions—remains a central challenge. Pioneering works leverage web-scale pre-trained backbones, V2X cooperation, LLMs for reasoning, and foundation models for broader generalization (Hwang et al., 2024, You et al., 2024, Zhang et al., 8 Jul 2025).
• Real-time Constraints and Scalability: Achieving deterministic low latency across diverse hardware remains challenging, especially with large foundation models or dense BEV computations. Sparsification, deformable attention, FlashAttention, and model distillation are active areas for optimization (Sun et al., 2024, Wang et al., 2023).
• Safety Guarantees, Certification, and Verification: Formal safety assurance and interpretability, especially for regulatory approval, are an open frontier. Hybrid approaches that overlay rule-based guards or planning solution verifiers upon end-to-end policies are under investigation (Lan et al., 2023, Chib et al., 2023).
• Multi-Agent and Cooperative Reasoning: Next-generation systems extend beyond single-agent reasoning to cooperative, multi-agent plans and interactive social behavior models embedded end-to-end (V2X-VLM, UniE2EV2X) (You et al., 2024, Li et al., 2024).
• Foundation World Models and Data Engines: The convergence of end-to-end planning with generative world models, simulation-based training, and automated rare-event search promises rapid advances in coverage and reliability (Chen et al., 2023, Singh, 2023).

7. Comparative Summary and Outlook

End-to-end autonomous driving systems are transitioning from proof-of-concept research to viable deployable stacks, as evidenced by the emergence of annotation-free, instance-centric, cooperative, and foundation-model–augmented planners. Empirical evidence demonstrates order-of-magnitude advances in efficiency, safety, and generalization, with leading systems (UAD, SparseDrive, TrajDiff, V2X-VLM) matching or surpassing highly engineered modular baselines (Guo et al., 2024, Sun et al., 2024, Gui et al., 30 Nov 2025, You et al., 2024). However, persistent open challenges—explainability, certainty quantification, edge-case robustness, and formal assurance—motivate ongoing hybridization of end-to-end and classical approaches, increased leverage of foundation models, and rigorous closed-loop simulation-based evaluation. The field is converging toward generalist models capable of multi-task reasoning, interpretable decision-making, and deployment-readiness in diverse real-world driving scenarios.