DriveTransformer: Unified Framework for Autonomous Driving

• DriveTransformer is a unified transformer architecture that integrates multi-view sensor perception, prediction, and tactical planning for autonomous driving.
• It employs sparse, query-centric attention with streaming temporal processing to efficiently fuse complex spatial and temporal information.
• The framework features uncertainty-aware decision modules that use entropy weighting to enhance safety and reduce collision rates in challenging driving conditions.

DriveTransformer is a collective term for a set of Transformer-based models and paradigms that implement end-to-end perception, prediction, and decision-making for autonomous driving. These models exploit the sequence modeling and self-attention capabilities of Transformer architectures to unify diverse driving tasks such as detection, mapping, prediction, tactical planning, control, and scene understanding. Recent advances emphasize sparse, query-centric attention, multi-modal integration, uncertainty modeling, and human-in-the-loop fine-tuning, positioning DriveTransformer frameworks as state-of-the-art baselines across simulated and real-world driving benchmarks.

1. End-to-End Unification and Task Parallelism

Modern DriveTransformer frameworks discard the traditional sequential pipeline (perception → prediction → planning) in favor of a unified stack of Transformer layers, where all agent, map, and planning queries directly interact at each block via self-attention (Jia et al., 7 Mar 2025). Multi-view sensor data and semantic queries (for objects, maps, and ego-centric planning) are embedded as fixed-size tokens. A standard block comprises:

• Sensor Cross-Attention: Task queries extract information directly from raw multi-view sensor tokens.
• Task Self-Attention: All task queries (ego, agent, map) attend to each other for synergistic information exchange.
• Temporal Cross-Attention: Queries access a queue of their historical embeddings for long-term temporal reasoning.

This design abolishes fixed feature hierarchies and dense BEV grid intermediate representations by allowing every subtask to flexibly access relevant information. As a result, planning-aware perception and prediction-aware planning feedback mechanisms emerge intrinsically at every block, avoiding error propagation and training instabilities inherent to staged pipelines (Jia et al., 7 Mar 2025).

2. Sparse Representation and Streaming Processing

DriveTransformer architectures eschew expensive dense rasterizations typical of BEV pipelines. Instead, all queries—originating from agent-centric, map-centric, or planning-centric heads—directly cross-attend to compact sets of sensor feature tokens. These are constructed by flattening and embedding multi-view image features, often extracted via convolutional backbones. Streaming processing is implemented by maintaining a FIFO queue of past queries; these are fused with current queries via temporal cross-attention, yielding efficient long-term scene memory (Jia et al., 7 Mar 2025). This approach drastically reduces attention complexity from $O(G^2)$ (where $G$ is the number of BEV grid points) to $O(N_q M + (1+T)N_q^2)$, where $N_q$ is the number of queries, $M$ the number of sensor tokens, and $T$ the temporal queue length.

The sparse, streaming paradigm both increases computational scalability and improves sample efficiency when operating over extended temporal horizons and large-scale multi-agent environments.

3. Uncertainty-Aware Decision Transformers

Variants such as the Uncertainty-Weighted Decision Transformer (UWDT) introduce principled uncertainty modeling into offline RL for tactical driving (Zhang et al., 16 Sep 2025). These models use a teacher-student paradigm:

• Teacher: A frozen Decision Transformer is trained on expert-generated trajectories to output next-action distributions given interleaved returns, state, and past actions.
• Per-token entropy: At each training step, the teacher’s output entropy $H_t = -\sum_{a} p_{T, t}(a) \log p_{T, t}(a)$ is estimated.
• Uncertainty weighting: Each token’s cross-entropy loss is scaled by a normalized, clipped power of $H_t$, amplifying the impact of rare, high-uncertainty, safety-critical states in the student’s learning objective:

$$L_S(\theta_S) = - \frac{1}{M} \sum_{t \in B_{\text{val}}} \bar{w}_t \log p_{S, t}(a_t)$$

where $$\bar{w}_t = \min\left( \frac{\tilde{w}_t}{\frac{1}{M}\sum_{j\in B_{\text{val}}} \tilde{w}_j}, w_{\max}\right)$$ and $\tilde{w}_t = H_t^\beta$ for calibrated $\beta$.

Empirically, such entropy-weighted objectives yield improved behavioral stability and collision reduction in dense, complex navigation scenarios, substantiating the claim that Transformer-based architectures can be selectively focused on rare, high-impact tactical maneuvers, overcoming the imbalance issues of offline datasets (Zhang et al., 16 Sep 2025).

4. Input Structures and Tokenization Strategies

Input representations span from bird’s-eye-view multi-channel occupancy grids (Zhang et al., 16 Sep 2025) and raw image sequences (Jia et al., 7 Mar 2025) to hierarchical fused features or patch embeddings (Dong et al., 2021). Canonical drives include:

• Occupancy Grids: Four-channel (occupancy, longitudinal/lateral velocity, road mask) grids processed via small CNNs, flattened, and projected into Transformer token space.
• Multi-view Images: Each view’s features are extracted, flattened, and provided as tokens with positional encodings.
• Semantic Queries: Each agent, map element, or planning target gets a dedicated query embedding, further incorporating spatial or temporal metadata.

Sequence modeling is performed either via autoregressive or generative Transformer decoders (for trajectory prediction (Zhu et al., 2022)) or via the direct mapping of state+history context to action outputs (for decision making or control). Streaming temporal attention integrates past context without recomputing full BEV features, enabling high-throughput online inference (Jia et al., 7 Mar 2025).

5. Loss Functions, Optimization, and Human-in-the-Loop Fine-Tuning

Optimization strategies include standard cross-entropy for action prediction, uncertainty-weighted cross-entropy for robust learning, and MSE or physically-motivated losses for trajectory prediction. Notably, recent frameworks combine behavior cloning pre-training with RL fine-tuning augmented by human guidance signals (interventions, demonstrations, corrective actions) (Hu et al., 2024). In these systems:

• Initial policy is fit via supervised regression on human demonstrations.
• Fine-tuning leverages reinforcement signals in simulation, with human overrides incorporated as prioritized transitions in the replay buffer and as a supervised loss term in the actor update.
• Reward shaping penalizes new human interventions to incentivize autonomous safe driving.

This hybridization accelerates convergence, reduces collision rates, and improves smoothness and generalization, as evidenced by nearly zero-collision and top success rates in simulated benchmarks (Hu et al., 2024).

6. Empirical Performance and Ablation Studies

DriveTransformer models deliver state-of-the-art results on major simulated (Bench2Drive, CARLA) and real-world (nuScenes) benchmarks across a range of metrics: trajectory L2 error, driving score, completion/success rates, average speed, and collision rates (Jia et al., 7 Mar 2025, Zhang et al., 16 Sep 2025). Ablations consistently show:

Component Removed	Driving Score or Success Change
Sensor Cross-Attention	Major drop (8.41% vs. 60.45%)
Task Self-Attention	Moderate drop (52.37% vs. 60.45%)
Temporal Cross-Attention	Moderate drop (56.22% vs. 60.45%)
No uncertainty-weighting (UWDT)	Collision rate spikes (6% vs. 1.25%)

Increasing the model scale (deeper/wider Transformer blocks) predominantly enhances planning ability. Comparative experiments demonstrate superior throughput (3x), reduced latency, increased robustness to sensor failures, and superior sample efficiency relative to previous baselines (Jia et al., 7 Mar 2025, Zhang et al., 16 Sep 2025).

7. Extensions, Related Paradigms, and Impact

Transformer paradigms have been extended beyond basic planning to include:

• Explicit object-centric detection and GRU-based trajectory planners (Detrive) for interactive planning-actuation interfaces, yielding improved collision avoidance over CNN-based perception (Chen et al., 2023).
• Explainable end-to-end systems with action + explanation heads leveraging global soft attention, offering interpretability via attention heatmaps and textual rationales (Dong et al., 2021).
• Driver activity recognition through hierarchical video transformers, robust to sensor/cabin configuration changes via latent space feature calibration (Peng et al., 2022).
• Long-horizon, generative trajectory prediction via encoder-decoder Transformers that avoid autoregressive error compounding, as exemplified in car-following models (Zhu et al., 2022).

This suggests that the Transformer framework, especially when formulated in sparse, query-based, and uncertainty-aware formats, subsumes existing staged pipelines, can directly produce interpretable, robust driving behaviors, and can flexibly adapt to diverse tasks from tactical planning to driver state recognition. The convergence of these advances in the DriveTransformer lineage substantiates a paradigm shift toward fully integrated, attention-centric autonomous driving policy architectures.