Driving on Registers, Reasoning on Risk: Risk-Aware Occupancy for Register-Based End-to-End Autonomous Driving

Jiaxing Chen; Hengduo Zou; YuKai Qin; Yiren Zhao; Lidong Yu; Bolin Gao

cs.AISep 18, 2026

Driving on Registers, Reasoning on Risk: Risk-Aware Occupancy for Register-Based End-to-End Autonomous Driving

Authors: Jiaxing Chen, Hengduo Zou, YuKai Qin, Yiren Zhao, Lidong Yu, Bolin Gao

Organizations: School of Vehicle and Mobility, Tsinghua University · The Hong Kong University of Science and Technology (Guangzhou) · Neolix, Beijing, China

Abstract

Multimodal trajectory prediction improves behavioral coverage in end-to-end autonomous driving, but existing methods remain limited by sparse scene representations. Incomplete evidence leads to low-quality candidate generation and unreliable ranking among geometrically similar trajectories. On a register-based baseline, bad and poor candidates constitute 19.74% of the candidate set, while the oracle-best candidate ranks only 33.9th on average. We propose RRDrive, which introduces risk-aware occupancy as a dense, temporally aligned, and trajectory-queryable representation. Its global structure guides high-quality multimodal generation, while candidate-conditioned risk queries support fine-grained selection. We further construct RiskOcc4D-NAVSIM with automatic risk annotations. RRDrive achieves a selected-trajectory PDMS of 0.951, representing a 1.5% relative improvement over the baseline (0.937), and improves the average candidate PDMS by 7.7%. In challenging scenes, it improves candidate PDMS by 30.2% and increases the Spearman correlation among good candidates by 0.41, from 0.26 to 0.67. To move beyond this oracle setting, we further develop an external RiskOcc predictor, a perception module that estimates risk-aware occupancy directly from sensor inputs. The competitive performance validates the representation's feasibility.

Figures & tables

Figure 1: Motivation and overall performance of RRDrive. Register-based baselines face both candidate generation waste and ranking errors in complex scenes, driven by unmodeled local future risks. Left: Multi-view inputs and temporal evolution of risk-aware occupancy. Right: Trajectory comparison and quantitative improvements validate our dense trajectory-queryable risk representation.

Figure 2: Overall architecture of RRDrive. A risk tokenizer encodes BEV risk-aware occupancy, which is fused with camera scene tokens for risk-conditioned candidate generation and read out along each candidate trajectory for risk-aware selection.

Figure 3: RiskOcc-conditioned candidate generation module. Risk tokens and camera scene tokens are combined by bidirectional cross-attention with LayerScale gating, and the fused context conditions the trajectory decoder so that the finite candidate budget is steered toward feasible, high-quality behaviors.

Figure 4: Risk-aware candidate ranking module. For each candidate, the ego footprint samples the time-aligned risk field along the trajectory to form a local risk profile, which augments the risk-sensitive scorer heads and sharpens ranking among geometrically similar good candidates.

Baselines & Oracle
Methods	PDMS $\uparrow$	NC $\uparrow$	DAC $\uparrow$	EP $\uparrow$	TTC $\uparrow$	Comf. $\uparrow$
Human Driver 2024	94.8	100.0	100.0	87.5	100.0	99.9
PDMS-Closed 2023	89.1	94.6	99.8	99.9	86.9	89.9
Ego-stat. MLP 2024	65.6	93.0	77.3	62.8	83.6	100.0
VLA-based Methods
UniVLA 2025	81.7	96.9	91.1	76.8	91.7	96.7

Table 1: Comparison of results on the NAVSIM benchmark. All metrics are higher-is-better. Best results are highlighted in bold. VLA-based and E2E methods are sorted ascendingly by PDMS.

\uparrow

\downarrow

: higher / lower is better.

Figure 5: Qualitative comparison of candidate generation on two generation-challenging scenes. Compared with DrivoR (third column), RRDrive (fourth column) concentrates the finite proposal budget within feasible, high-quality regions aligned with the expert trajectory, producing more good candidates and higher mean candidate PDMS.

Variant	Module	NC $\uparrow$	DAC $\uparrow$	DDC $\uparrow$	TTC $\uparrow$	EP $\uparrow$	Comf. $\uparrow$	PDMS $\uparrow$	$\Delta$
Baseline (no RiskOcc)	–	99.02	98.92	97.23	96.72	89.91	100.00	93.67	–
+ global RiskOcc encoder	Gen.	99.24	99.08	97.51	97.34	90.96	99.99	94.49	$+0.82$
+ trajectory-conditioned sampling	Rank.	99.30	99.02	97.56	97.57	91.25	99.99	94.68	$+0.19$
+ bidirectional cross-modal fusion	Gen.	99.37	99.04	97.46	97.78	91.51	100.00	94.90	$+0.22$
+ TTC/EP & RiskOcc refine	Rank.	99.34	98.98	97.41	97.55	92.19	100.00	95.06	$+0.16$

Table 2: Cumulative ablation on navtest (argmax, no re-ranking), decomposed into all PDMS sub-metrics. Module tags the primary pathway each component serves: Gen. = candidate generation, Rank. = candidate ranking. All metrics are higher-is-better; NC = no at-fault collision, DAC = drivable-area compliance, DDC = driving-direction compliance, TTC = time-to-collision, EP = ego-progress, Comf. = comfort.

\Delta

is the absolute PDMS gain over the preceding row. Rows are model-lineage proxies: each step carries minor co-changes, so tags denote the dominant pathway rather than an exclusive one.

Metric	Baseline	Ours	$\Delta(\%)$
Good ratio $\uparrow$	80.3%	88.7%	$+10.5$
Bad ratio $\downarrow$	13.6%	7.6%	$-44.1$
Mean cand. PDMS $\uparrow$	0.797	0.858	$+7.7$
Good cand./scene $\uparrow$	51.4	56.8	$+10.5$

Table 3: Candidate generation quality.

\uparrow

\downarrow

: higher / lower is better;

\Delta(\%)

is the relative change over the baseline.

Metric	Baseline	Ours	$\Delta(\%)$
Scene Spearman $\uparrow$	0.407	0.584	$+43.5$
Oracle recall@1 $\uparrow$	5.1%	15.4%	$+202.0$
Oracle recall@5 $\uparrow$	12.2%	29.3%	$+140.2$
Oracle rank $\downarrow$	33.9	27.7	$-18.3$
Oracle gap $\downarrow$	0.057	0.034	$-40.4$
Selected PDMS $\uparrow$	0.937	0.951	$+1.5$

Table 4: Scene-level ranking quality.

\uparrow

\downarrow

: higher / lower is better;

\Delta(\%)

is the relative improvement over the baseline.

Figure 6: Qualitative comparison of candidate ranking. Within dense bundles of geometrically similar, high-quality candidates, RRDrive selects a near-optimal trajectory where the baseline (third column) mis-ranks and selects a low-scoring one, isolating the improvement to ranking rather than to candidate generation.

Feature set	Ego-progress $\uparrow$	True PDMS $\uparrow$
Geometry only	0.369	0.371
RiskOcc only	0.174	0.177
Geometry + RiskOcc	0.535	0.533

Table 5: Within-good-candidate ranking under scene-grouped cross-validation (mean scene-wise Spearman

\uparrow

): the risk profile adds ranking information beyond trajectory geometry.

Scenario	Metric $\uparrow$	Baseline	Ours	$\Delta(\%)$
Gen-hard	Candidate PDMS	0.540	0.703	$+30.2$
Gen-hard	Good ratio	50.1%	69.8%	$+39.3$
Rank-hard	Scene Spearman	0.072	0.497	$+590.3$
Rank-hard	Selected PDMS	0.816	0.884	$+8.3$
Confu-hard	In-good Spearman	0.265	0.674	$+154.3$
Confu-hard	Oracle recall@1	3.6%	24.9%	$+591.7$

Table 6: Hardest-tercile analysis (baseline vs. Ours). All listed metrics are higher-is-better (

\uparrow

);

\Delta(\%)

is the relative improvement over the baseline.

End-to-End Autonomous Driving

End-to-end autonomous driving learns to turn sensor observations and driving context into vehicle actions or trajectories.

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Risk-Aware Occupancy for Safety-Oriented End-to-End Autonomous Driving

Conventional end-to-end driving systems model the environment with sparse objects and lane elements. While efficient, this paradigm discards planning-critical information in crowded and occluded scenarios, particularly for unstructured obstacles, ambiguous free space, and complex interactions. We propose risk-aware occupancy, a dense BEV representation that explicitly fuses geometric occupancy, map-derived traffic constraints, and future dynamic-agent occupancy as complementary risk signals. Built upon this representation, we develop ROIDrive, an instance-centric end-to-end framework with a dedicated risk-aware occupancy branch. The predicted occupancy is tokenized via sliding-window sampling and injected into planning queries via cross-attention, while temporal query consistency mitigates unreliable flickering queries. We also contribute RiskOcc4D-nuScenes, a benchmark derived from nuScenes and Occ3D-nuScenes with four automated annotation pipelines for multi-dimensional risk supervision. Experiments on representative occupancy architectures verify the learnability and transferability of our representation. Integrated with GenAD, it reduces collision rates by 35.0% (UniAD metric) and 52.9% (ST-P3 metric), confirming the efficacy of the proposed representation modality.

Jiaxing Chen, Hengduo Zou, Yiren Zhao +1

School of Vehicle and Mobility, Tsinghua University · The Hong Kong University of Science and Technology (Guangzhou)

End-to-End Autonomous Driving 3D Occupancy Prediction Autonomous Driving Benchmarks

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Sep 9, 2026

Data-Driven Risk Fields for Safer End-to-End Autonomous Driving

Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities. Existing rule-based risk models provide interpretable safety priors, yet their absolute risk scores depend on handcrafted functions, coefficients, and thresholds. Learning-based risk representations reduce part of this manual design, but their supervision often relies on occupancy-derived labels or heuristic cost values, which may not capture ego-conditioned planning risk. In this paper, we propose DRiF, a data-driven risk-field framework for safer end-to-end autonomous driving. DRiF learns a shared BEV feature with static map segmentation, dynamic risk prediction, and vehicle planning. For dynamic risk learning, DRiF converts rule-based safety priors into pairwise risk labels, and trains the risk field to preserve relative risk ordering instead of regressing handcrafted absolute scores. Experiments on Bench2Drive show that DRiF achieves competitive overall performance, with consistent improvements in driving score, success rate, and collision-related metrics. These results establish relative risk supervision as an effective way to connect explicit safety structure with end-to-end planning. The data and code will be publicly available.

Yuanxin Tian, Zhiyuan Liu, Jinhao Li +9

Tsinghua University · Yinwang · KAIST

End-to-End Autonomous Driving Safe Motion Planning Autonomous Driving Planning

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Oct 27, 2026

Structured Representation Probing for Risk Anticipation in End-to-End Autonomous Driving

End-to-end driving policies form structured internal representations of traffic, ego behavior, and road constraints. These representations offer a monitoring interface for anticipating failures, complementing risk signals from trajectories, controls, and manually designed features. We introduce the Structured Representation Probe (SRP), a lightweight monitor that reads actor, ego, and map representations from a frozen policy. SRP learns a separate linear risk direction for each source and aggregates element scores through cardinality-normalized log-mean-exp pooling, retaining sensitivity to salient evidence while reducing dependence on token count. Event-window supervision links the readout to upcoming hard failures, and auxiliary event type heads jointly train the source directions across failure modes. Online alerting uses a single generic score without modifying the driving policy. Bench2Drive experiments on DriveTransformer, UniAD, and VAD show higher frame-level PR-AUC and ROC-AUC than the evaluated runtime baselines. On DriveTransformer at a horizon of 2 seconds, ROC-AUC reaches 0.833 versus 0.715 for temporal consistency, with 2.67% readout runtime overhead. The trained probes also retain the ability to distinguish impending failures on new routes.

Jiawei Liang, Tianrui Lou, Le Yang +3

School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China · School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China

Autonomous Driving Autonomous Driving Safety Evaluation Representation Probing End-to-End Autonomous Driving

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End-to-End Autonomous Driving

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