Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving
Authors: Kairui Ding
Boyuan Chen
Yuchen Su
Huan-ang Gao
Bu Jin
Chonghao Sima
Xiaohui Li
Wuqiang Zhang
Paul Barsch
Hongyang Li
Hao Zhao
Venue: CoRL 2024 (2024)
Keywords: Interpretability
Language alignment
Autonomous driving
TL;DR: Aligning natural language to intermediate states of autonomous driving model for aligned interpretability and state-of-the-art captioning accuracy.

Abstract: End-to-end architectures in autonomous driving (AD) face a significant challenge in interpretability, impeding human-AI trust. Human-friendly natural language has been explored for tasks such as driving explanation and 3D captioning. However, previous works primarily focused on the paradigm of declarative interpretability, where the natural language interpretations are not grounded in the intermediate outputs of AD systems, making the interpretations only declarative. In contrast, aligned interpretability establishes a connection between language and the intermediate outputs of AD systems. Here we introduce Hint-AD, an integrated AD-language system that generates language aligned with the holistic perception-prediction-planning outputs of the AD model. By incorporating the intermediate outputs and a holistic token mixer sub-network for effective feature adaptation, Hint-AD achieves desirable accuracy, achieving state-of-the-art results in driving language tasks including driving explanation, 3D dense captioning, and command prediction. To facilitate further study on driving explanation task on nuScenes, we also introduce a human-labeled dataset, Nu-X. Codes, dataset, and models are publicly available at https://anonymous.4open.science/r/Hint-AD-1385/.

Hint-AD: Holistically Aligned Interpretability in End-to-End Autonomous Driving
Kairui Ding Boyuan Chen Yuchen Su², Huan-ang Gao', Bu Jin', Chonghao Sima?,
Wuqiang Zhang", Xiaohui Li", Paul Barsch", Hongyang Li Hao Zhao?-l
Institute for AI Industry Research (AIR) Xinngiaan College, Tsinghua University
Mercedes- Meenee Group China Ltd. OpenDriveLab, Shanghai Al Lab
f Indicates corresponding author
Language
Language decoder
TEXT right turning
Trick
A red car detected tothe right...
The speed of front car mix.
The epo car making right turn.
Metion
Internediate
Command
Multi-ies
Autonomous driving model
image
Perception
Prediction
Planning
Multiiiwn
Autonomous driving model
image
Declarative inteppetabilltty
(b) Interpetabte
Figure 1: Illustration of two paradigms for interpretability of end to-end autonomous driving (AD) systems through natural language. (a) The declarative interpretability does utilize intermediate outputs from AD systems, resulting in text that merely justifies the car's driving behavior; (b) Aligned interpretability incorporates intermediate outputs from the AD model for alignment.
Abstract:
End-to-end architectures in autonomous driving (AD) face a significant challenge in interpretability, impeding human-Al trust. Humaan-rrinndly natural language has been explored for tasks such as driving explanation and 3D captioning. However, previous works primarily focused on the paradigm of declarative interpretability. where the natural language interpretations outputs of AD systems, making the interpretations only declarative. In contrast, aligned interpretability establishes a connection between language and the in- termediate outputs of AD systems, Here we introduce Hint-AD, an integrated AD-language system that generates language aligned with the holistic perception- prediction-planiigg outputs of the AD model. By incorporating the intermediate outputs and a holistic token mixer sub-network for effective feature adaptation, Hint-AD achieves desirable accuracy, achieving mate-offthheart results in driving language tasks including driving explanation, 3D dense captioning, and command prediction. To facilitate further study on driving explanation task on nuScenes, we also introduce a human-labeled dataset, Nu-X. Codes, dataset, and models are publicly available at http:://aaadddicorrieee github. io/ ///nt-intDDa.///
not grounded in the intermediate
Keywords: Interpretability, Autonomous driving, Language alignment
Introduction
End-to-end perception- perception-plannigg architecture is critical in autonomous driving (AD) [1, 2] and general embodied intelligence [3, 4] due its potential for self-supervised training with extensive data. However, these systems face significant interpretability challenges [5, 6].
Interpretability issue [7, 8, 9, 10, 11] is particularly pronounced in embodied intelligence problems such as AD. When an AD system directly outputs control signals, it becomes difficult for human
8th Conference Robot Learning (CoRL. 2024), Munich, Germany.

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passengers to trust the decisions. To address this, natural language, a highly user- friendly communi- cation medium, has been explored to enhance interpretability through tasks like driving explanation, 3D dense captioning, and visual question answering (VQA). While human driver recognizes the value of the bird's eye view (BEV) trajectories as an explanation of WHAT happening, language offers complementary perspective on WHY this happening. These approaches can be categorized into declarative interpretability and aligned interpretability based on single criterion: whether the generated language aligns with the intermediate outputs of the AD system (Fig. 1).
Declarative interpretability generates natural language directly without intermediate inputs from the AD system, as recent works on driving explanation [12, 13, 14], 3D dense captioning [15]. and visual question answering [16, 17, 18, 19]. This approach often suffers from hallucination, the language is not grounded in comprehensive intermediate outputs, making mere justification of the driving behavior.
Aligned interpretability requires alignment between language and the inner states of the AD model. To our knowledge, this approach was first explored by [14], aligning attention states of the AD model with language decoder. Later works aligned the language decoder with the inner decision states [20].
However, existing research neglects the correspondence between the language decoder and the complete
outputs of an AD pipeline, resulting in a discrepancy between the language tasks and AD tasks. The potential to enhance the accuracy of language tasks in driving scenes through intermediate AD pipeline outputs remains unexplored. To this end, we propose Hint-AD, an integrated AD-language framework designed for holistic alignment with the AD model's pnrception-prediiction-laaan
process and high-aceuracy language generation to facilitate the interpretability in autonomous driving.
We developed two approaches for the holistic alignment between language and the AD model and the accuracy of language outputs: (a) We develop a holistic token mixer module that adapts the intermediate output tokens from the AD model for the language decoder, focusing on robust feature extraction and fusion; (b) We introduce an alignment task as an online dataset align the language output with the intermediate outputs of the AD model, requiring the language decoder to interpret intermediate tokens generated during the AD model's inference process throughout training.
We implemented Hint-AD on both UniAD [2] and VAD [21], state-of-the-an (SOTA) AD models utilizing rasterized and vectorized representations, respectively, to demonstrate its generality. Ex- perimental results show that Hint-AD achieves state-of-the-arr performance on various language tasks, including driving explanation (20.4% on CIDEr than baseline), 3D dense captioning (185% on CIDErthan baseline), VQA (1.2 percent improvement accuracy). and driving command prediction (1.2 percent improvement of accuracy). The alignment tasks significantly improved coherence be- tween language outputs and intermediate AD model representations. Additionally, we contributed a human-labeled driving explanation dataset, Nu-X on nuScenes [22] to address the lack of driving explanation data on this widely-used AD dataset.
2 Related Works
End-to-end autonomous driving systems aim to create an architecture that processes sensor data and directly outputs vehicle control signals [23]. These systems have gained research attention due to their ability to address error accumulation problems found in traditional modular designs, where perception and planning are separated into distinct modules [24, 25, 26, 27, 28, 29, 30, 31, 32). Prominent examples include UniAD [2] and VAD [21] integrate modular perception tasks such as object tracking, map building, motion forecasting, and trajectory planning within a unified framework. Offline datasets for end-to-end autonomous driving have also been developed [22, 33].
Interpretability of AD [5, 6, 7, 8], the ability provide comprehensive explanation for AD planning is crucial for user trust and system transparency in AD systems [5, 6]. Natural language, as

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user-friendly medium for communicating with users, has been explored for improving interpretability of AD through by means like driving explanation [12, 13, 14), VQA [16, 17, 18, 19], and 3D dense captioning [15]. Previous work mainly focused on declarative interpretability, For example, [12, 34] realized driving explanation tasks using visual information. But intermediate outputs from AD model not aligned. [14] raised the concept that the language output should be grounded on the inner states of AD system. Aligning the decision state between the language decoder and the inner states of AD model has also been explored [35], but to our knowledge, no previous work has achieved holistic alignment with all perception-prediction plannnng process with an AD model.
3 Methodology
To explore holistic aligmment between natural language and intermediate results in end-to-end AD frameworks, we propose novel framework named Hint-AD, which consists of three modules: holistic token mixer, a language decoder, and traditional AD framework. An overview of Hint-AD is shown in Fig. 2. The existing AD pipeline in Fig. 2 can be any end-to-end AD system that decomposes AD into perception, prediction and planning. Without loss of generality, we implement our method on top of both UniAD [2] (as Himt-UnAAD and VAD [21] (as Hint- VAD), which use rasterized and vectorized representations, respectively.
3.1 Overall framework of Hint-AD
Firstly, we extract intermediate query tokens from the existing AD model of a prrdcctonn-rdiictiion planning architecture, yielding track tokens, motion tokens, and planning tokens. Secondly, a holistic token mixer module will adapt the tokens for language decoder input, in which we design an instance mixer to merge instance-level track and motion information of each detected instance. We also introduce BEV blocks and instance blocks for further feature extraction and converting length-variable instance tokens to fixed length. All the processed tokens are concatenated as context tokens for text generation (see Sec. 3.2). Finally, the context tokens are formulated as prompt tokens and put into the language decoder together with text prompts. We adopt barbell adaptation paradigm for efficient context understanding of the language decoder (see Sec. 3.3).
For aligning language and the intermediate results of the AD pipeline training, we incorporate extra training data called alignment task, which is constructed online during training (see Sec. 3.4). Additionally, the training procedures are described in Sec.3.5.
3.2 Holistic token mixer
The query tokens extracted from the AD pipeline are not directly understandable to the language decoder. Addressing this, we propose a holistic token mixer architecture. The implementation is slightly different between Hint-UniAD and Hint-VAD in detail. We primarily follow the design of Hint- while small adjustments for Hint-VAD are provided in Appendix B.3.
We start by giving denotations for query tokens extracted from the AD pipeline. For typical purception-predittion-alae
AD pipeline, we can extract the following components: BEV tokens Fbev E H where Hr, W., and C are the height, width, and channels the BEV field. contain position and past trajectory information each detected object, where Náct the number of detected objects, and D is the dimension of the token vector. Motion tokens {Fr (Fmmotion)) J C RDD contain predicted future trajectories of each detected object. Planning steps Fplan E x2 would be the future trajectories predicted by the model.
NNa C C Track tokens )
To effectively merge tokens into an instance level, we design a novel instance mixer that integrates the track token Firack and the motion token Fi of each detected instance into an instance token Få Finstance. This is accomplished through tensor concatenation followed by Multi-Layer Perceptron (MLP) projector Piustauce to project tokens of Naet detected instances into embeddings with dimension Dembed:
Fi F Pinstance (Concat(FFrrck, Frrcck F Fmotion)) for i ......., Naet.
(I)
3

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Feature mncdder
X
Dehovior The
Project
mainiins sosiht courte becuse dbsucks are present chord.
Malti Beddhadd self aitentiionn
BEV
Nora
Norm
MLP
Instance
Text * deander
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Tranformer encoder
(b) BEV blocks architocture
Pre trained MLLM barbell adaptation
Prediction
Cuncatenate
Adapter
prompt
lie bdtanter rove
Reharae cpplaminy-SSaa
Teat encodlrr
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+
Prozen parumeters
RPV Inionn
Pgo motion
Test tokan
Track tokeen
tradk token
Leurnable
instance
Transformer
(a) Hi pipeline illustration
(c) Instance blocks architecture
Figure 2: Framework of Hint-AD. (a) Hint-AD pipeline illustration. Taking intermediate output tokens from an AD pipeline as input, a language decoder generates natural language responses. A holistic token mixer module is designed to adapt the tokens. (b) Detailed illustration of BEV blocks architecture. (c) A detailed illustration of instance blocks architecture.
Feature encoder E is implemented as a multi-layer convolutional network, which extracts the feature and down-scales the BEV to 3 x 3. Following this, an MLP projector Pey employed to transform the BEV channel dimension C' into Dembod, yielding Phev Prev (E(Fbev))
BEV blocks and instance blocks employ multi-head self-attention layers to adapt BEV and instance features. For BEV tokens, multi-head self-attention (MHSA) operates among them. Given the variable number of detected instances per frame, -Vin-adap: learnable tokens (Finsaddapt } f=1 introduced as queries. Multi-head cross-attention (MHCA) is then performed between these learnable tokens and the instance tokens. BEV and instance blocks improve adaptation through improving feature extraction and fusion of BEV and instance tokens, as demonstrated in Sec. 4.4.
Minesadage
Fhe = MHSA(F(e))
F F'ustance MHCA (FnCCA(ii-aadapt. ins-adapt: Finstance). )
The planning steps Fplan would be encoded by sinusoidal positional encoding PE and an MLP projector Pplau to embedding dimension Dembed: F'laan Pplau (PE(Fplan)).
Among all the instances tokens, there's one ego instance token instancee representing the ego car [2]. Processed BEV, instance, ego instance, and planning tokens would be concatenated as context tokens for further language generation tasks:
pogo
Poontext = Concal (Frev: Fl Finstance:
pogo instance?
FV
plan))
3.3 Language decoder with barbell adaptation
To incorporate high-level reasoning and context understanding ability of Multimodal Large Language Models (MLLMM)) in AD relevant language tasks [36, 37, 17, 18, 38, 39], we employ LLaMa- Adapter-V2 V2 [40] as our language generator with a pretrained LLaMA-2-7B [41]. For language fine-tuning, learnable adapters (Fi (Fadptter) Mudaatee C Raee are employed, which serve extra keys and values in the inserted layers with zero-initialized attention [40], where Nadapter is the number of layers to insert, and Decc is the dimension of tokens in the language decoder.
In the original LLaMa- Aaatter-ddpte---Va V2 V2 strategy, context tokens Frontext would be inserted in the first layer, and learnable adapters would be inserted all other N layers, where N is the total number of layers of LLaMA-2-7B. We observed that the adapters, which are for language tuning, tend to dominate the adaptation process and reduce the context- language alignment. This is crucial for AD tasks that requires high-level context understanding ability. Thus propose barbell adaptation

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paradigm (see Fig. 2). where learnable adapters are inserted only at the Ntoot + 1] layers (as fromt adapters { ifrn ) Niront) ) and the [N Nend + 1, N] layers (as end adapters { Fr cnd berr). ). The
context tokens are inserted at the first layer.
The rationale for placing adapters both the front and end is that front adapters aid in comprehending context information, while end adapters enhance language fine-tuning. This design balances the need for high-level context understanding and precise language adaptation. The effectiveness of this barbell adaptation approach is demonstrated in Sec. 4.4. During training, we employ cross-entropy loss as the captioning loss, with supervision applied exclusively to the answer tokens.
3.4 Aligning language and intermediate outputs
To align language with intermediate outputs from AD model, the language decoder needs grounded context understanding of the information contained in each token (i.e. object's 's position in track tokens) generated in AD model's inference steps. We implemented this by adding an online alignment task dataset the training process.
During the alignment task, given the AD model's intermediate inputs, set of prompt-answer pairs generated (see Fig. 3). This task includes four types of alignments: (a) Counting alignment, requiring the language decoder to interpret the number of instances of each type detected in the frame according to track tokens; (b) Position alignment, necessitating the model to provide the position of a tracked instance based on a specific instance token; (c) Motion alignment, involving decoding the velocity information contained in an instance token; and (d) Planning alignment, requiring the language decoder to output the future trajectory points contained in planning token.
All the ceestion-annwer pairs for alignment tasks are generated online during training. The alignment tasks greatly improve the language decoder's 's context understanding of the intermediate tokens, thus improving the accuracy of AD captioning by large margin (see Sec. 4.4).
3.5 Training pipeline
The whole training pipeline of Hint-AD includes two stages. In the first stage, the end-to-end AD model is trained independently. In the second stage, freeze all parameters of the AD model and the MLLM parameters, updating only the parameters of the holistic token mixer and adapters. The total trainable parameters at the second stage are 87M, more training details are stated in Appendix A.
4 Experiments
4.1 Datasets and baselines
Explanation serves as guide for human learning and understanding [42, 43). Particularly in the context of end-to-end autonomous driving (AD) systems, human users often seek explanations to bridge the gap between sensor inputs and AD behaviors [14]. Currently, there is no dataset providing such explanations for nuScenes [22], a widely utilized dataset in AD research. To address this gap and facilitate interpreabillityfocusse research nuScenes,
Datasets.
introduce Nu-X, comprehensive, large-scale, human-labeled explanation dataset. Nu-X offers detailed contextual information and diverse linguistic expressions for each of the 34,000 key frames in nuScenes.
sentence of explanation typically comprises narration and reasoning [14), for instance: "<Narration>> The car is merging into the right lane. <Reasoning>> To pass the red car front." In our dataset, each caption encompasses these two components. Detailed examples and key data statisties presented in Fig. 10. For in-depth description of the labelling process and comprehensive data statistics, please refer to Appendix D.
All Hint-AD architectures and baselines were trained and evaluated on the following datasets to provide a comprehensive analysis: (1) Alignment task dataset (see Sec. 3.4), designed align lan- guage with intermediate outputs of the AD model by requiring the language decoder to interpret each intermediate token, with ground truth answers generated online during training; (2) TOD Cap [15], 3D dense captioning dataset offering object descriptions for 64.3K outdoor objects in nuScenes,
5

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GAM, FROM
GAM
RIGHT
BEV mupping
4HEAD
RIGHT
Driving explanation
3D dense captioning
VQA
Question
Pred Narration: The right slouly
Object
What moving thing the front of me?
the intersection.
Reasoning: To enter the road on the right.
Pred
white, shiny, and sleek about away front right ego not moving in the driving lane. white, shiny, sleck away the front right ego car not moving in the intersection.
GT
Pred
bus
GT
Narration: The vehicle smoothly executess
Command prediction
right
GT
5
Ressoning: navigate adjacent road on right.
Pred
TURN RIGHT and DECELERATE
GT
TURN RIGHT and DECELERATE
Alignment tasks
Type Position
Type Object
Type Planning
Type Counting
Object
Pred
The trajectory points
Pred
Pred
Pred
cars bus podestrians
object meters away at dock direction.
The moving at o'clock direction.
3.71./a iel so]]].
detected in the seene.
GT
GT
GT
GT
The polnts are
There cars hos pedestrians
The about
The object moving
30..0..5s...33-3. 38.6(23666.21))
defected
away at o'oock direction.
Figure 3: Qualitative Results. We present examples of the language output generated by Hint-AD across multiple tasks, including driving explanation, 3D dense captioning, VQA, command prediction, and four categories of alignment tasks. Captions that do match the ground truth colored red.
annotated with appearance, motion, environment, and inter-object spatial relationships; (3) NuScenes- QA [16], VQA dataset covering 34K frames of nuScenes with five question types, including existence, counting, query-object, query-status, and Comparison; (4) Driving command dataset, labeled by us on nuScenes (see Appendix E), composed of direction and velocity commands.
Baselines. We have selected benchmark methods that include both key milestones and state-of-the the art approaches in language generation within the AD context (more details in Appendix C.4): (1)
ADAPT [12] generates sentences with a vision-language transformer in an auto-regressive manner. Cross attention and sparse attention masks used text and video tokens; (2) BEV+Adapter [40] V2 (the same Hint-AD) as the language decoder; (3) BEVDet+MCAN [16] uses Modular Co-Attention Network (MCAN) [44] with layers of self-attention for separate language and visual understanding. Stacked cross-attention layers are used for cross-moder feature interaction. Detection results from BEVDet [45] is adapted for input; (4) Vote2Cap-DETR [46] has two parallel task-specific heads base on a transformer architecture. The queries are decoupled into localization queries and caption queries; (5) TOD Cap [15] utilizes query-based detection head to generate
takes only BEV features input and LLaMA-
of 3D object proposals from the BEV features. These features are then processed by LLaMA-Adapter [47] to be prompts for the language model to generate dense captions; (6) GPT-4o is a multimodal model developed by OpenAI, equipped with state-of-the art vision capabilities alongside text generation performance comparable to its predecessor, GPT-4. (7) Gemini-1.5 is a pioneering large language model from Google, specifically designed to handle multimodal inputs with an extended context length.
4.2 Comparing with baseline models
Quantitative results. We present results separately different input types and backbone modules on four datasets. For Nu-X and TOD*Cap datasets, we adopt four standard image captioning metrics, CIDEr (C) [48], BLEU (B) [49]. METEOR (M) [50] and Rouge (R) [51]. GPT-3.5 scoring (G) is also used for Nu-X" evaluation due the comprehensive expression in driving explanation (Sec

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Appendix C.6 for detail). A threshold of 0.25 is set for matching predicted and ground ground-truth truth bounding box while testing COp For NuScenes-QA and Command datasets, we directly compare the generated texts with ground truth to obtain accuracy. Based on reasoning complexity, QA are divided into zero-hop (HO) and one-hop (HI). Following conclusions can be drawn from Tab.
Both Hint- UniiD and Hint-VAD demonstrate high performance multi-task tests. Both systems achieve SOTA results on the Nu-X dataset, surpassing the best baseline (BEV+Adapter) by 3.8 points (20.4%) in CIDEr scores. Remarkably, Mint-UniAD exhibits significantly superior performance on the Tap task, with a CIDEr score improvement of 222.3 points (185%). Although Hint- VAD performs slightly lower on this task, potential explanations are discussed in Appendix C.3. Additionally, on the NuScenes- QA and Command datasets, Hint- VAD achieves overall accuracy improvements of 0.6 and 1.2 points over the best baseline, respectively. These results underscore the effectiveness of the proposed Hint-AD architecture.
Table 1: Comparison with Baselines. "Inter. outputs" represents intermediate outputs. All methods are adapted for BEV visual representation and employ mixed dataset training. Hint-UniAD and Hint-VAD, as two implementations of Hint-AD on different AD models, outperform baselines across four language tasks the AD context.
Input
Method
Nu-X
TCp
NuScenes- QA
Command
C1
B1
M
R
G1
C1
B1
M
R
HD 1
H1
All
Acc. f
Image
GPT-40
19.0
3.95
10.3
24.9
5.22
160.8
50.4
31.6
43.5
42.0
34.7
37.1
75.4
6-shot examples
Gemini
17.6
3.43
9.3
23,4
5.03
169.7
53.6
33.4
45.9
40.5
32.9
35.4
80.9
BEV(2D
REV+Adapee (40)
ADAPT [12]
17.7
18.6
2.06
3.47
12.8
11.3
24.5
27.9
5.79
51.0
45.6
44.2
47.7
46.4
81.1
79.3
bounding hoxes
BEV
Vo6:2Cap-EETR [46]]
BEADei-MCAN (44]
13.2
15.3
2.91
2.61
10.9
10.3
24.5
24.2
5.04
5.33
110.1
104 9
50.1
48.0
44.4
43.0
68.0
67.8
56.2
51.2
46.7
44.9
47.0
49.9
80.7
76.5
Top [15]
14.5
2.45
10.5
23.0
120.3
51.5
45.1
70.1
53.0
45.1
49.0
78.2
BEV
Hint-UniAD (Ours)
21.7
4.29
12.7
27.0
7,20
342.6
71.9
48.0
85.4
56.2
47.5
50.4
83,0
outputs
Hin--VAD (Ours)
22,4
4.18
13.2
27.6
7.44
263.7
67.6
47.5
79.4
55.4
48.0
50.5
82.3
Qualitative Results. Figure 3 presents some qualitative results. The text generated by Hint-AD demonstrates a grounded understanding of the scene and aligns appropriately with the intermediate results of the AD model. For additional results and analysis, please refer to Appendix C.2.
4.3 Analysis alignment between language and AD model
To quantify the alignment between language and the intermediate outputs of the AD model, we evaluate the language decoders' output against the predictions of the AD perception modules, which is generated online on the validation set. Four kinds of disalignment protocols are designed: (a) Counting Disalignment (CD) which measures the discrepancy between the number of instances in each category given by the decoding head and the tracking model, (b) Position Disalignment (PD) which measures the positional difference of a specific instance, (c) Motion Disalignment (MD) which measures the velocity difference, calculated as the mean distance between the velocity in the caption and the velocity predicted by the perception system, and (d) Planning Disalignment (PLD) which measures the discrepancy in trajectory points. For detailed definition, please refer to Appendix C.I.
We tested Hint-AD with both aligned interpretability (original design) and declarative interpretability, results are detailed in Appendix C.I. The aligned language decoder performs significantly better than the models operating under the declarative interpretability paradigm, indicating the effectiveness of alignment designs including holistic token mixer and alignment tasks.
4.4 Ablation study
Effectiveness of holistic alignment. To evaluate the effectiveness of holistic language-AD alignment on language task accuracy, we conducted an ablation study by removing track, motion, and planning tokens from the language decoder's inputs. Results in Tab. 2 show that using all tokens achieves the highest performance. Track tokens enhance 3D dense captioning with positional information, while planning tokens improve command prediction by providing future trajectory data.
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Table 2: Ablation on holistic alignment. The performance highest when all tokens are used, highlighting their importance in enhancing specific tasks.
Nu-X
Top
NuScenes-Qa QQA
Command
Method
Input
B
Ct
B
H01
Wo track token
19.5
2.95
11.3
21.3
6.85
120.3
48.0
48.5
69.4
53.0
42.6
46.1
82.5
Hint-UniAD
mution
W/o planning token
20.8
3.10
11.4
23.5
6.39
290.6
68.4
44.5
85.6
55.3
46.7
49.6
79.1
all tokens
21.7
4.20
27.0
7,20
342.6
71.9
48.0
85.4
56.2
47.5
50.4
83.0
Ablation on Holistic Token Mixer Design. Instance mixer and instance blocks enhance the feature extraction and adaptation of intermediate tokens. Results in Tab. 3 indicate that removing instance blocks and instance mixer significantly reduces performance TOD*Cap and NuScenes-QQ QA, the positional and motion information of objects is not adequately fused.
Table Ablation on holistic token mixer. The performance is highest when all sub-networks are included, showing the importance of these components for effective feature extraction and adaptation.
Nu-X
TOD'Cap
NuScenes-QA
Command
Method
Sub-networks
C1
M
1
R1
G1
C1
B1
M
R1
H01
I11 1
All
Acc.
Wio instance miser
20.8
3.22
10.6
26.5
6.38
220.4
58.3
46.9
73.0
52.0
44.8
47.2
81.6
Hint-UniAD
Wo instance blocks
21.3
3,24
11.3
27.1
7.13
259.4
62.5
47.0
82.3
54.3
47.2
49.6
81.3
All sub-networks
21.7
4,20
12.7
27.0
7.20
342.6
71,9
48.0
85.4
56.2
47.5
50.4
83.0
Effectiveness of barbell adaptation. We explore three alternatives: (a) Early Fusion, original design of LLaMA-Adapier- [40], adapters in each layer; (b) Pyramid adaptation, adapters in the first Ntrout layers; and (c) Hammer adaptation, adapters in final Nend layers only. Barbell adaptation performs the best on three datasets (Tab. 4). More adaptation methods in Appendix B.2.
Table 4: Ablation on adaptation strategy. Ballbell adaptation achieves the best scores on all datasets except for COp demonstrating adaptation strategy enables better context understanding and language fine-tuning.
Method
Adaplation Strategy
Nu-X
TOD
NuScenes QA
Command
B1
G1
R
Acc.
Early fusion
16.9
3.34
12.0
25.6
6.94
350.4
70.6
45.3
89.3
47.0
49.9
78.3
Hint-UniAD
Pyramid adaptation
15.3
3.10
11.2
24.4
6.34
335.3
70.8
44.4
67.8
52.5
44.6
48.5
77.5
Hammer adaptation
2.45
23.0
6.19
298.7
65.6
42.1
60.1
43.1
76.2
Barbell aduptation
21.7
4.20
12.7
27.0
7.20
342.6
71.9
48.0
85.4
56.2
47.5
50.4
83,0
Conclusions and Limitations
We present Hint-AD,, integrated AD-language framework that aligns language generation with perceppioon-pediicioon prediction-plaming process of AD models, achieving SOTA performance
the holistic perception- in multiple AD captioning tasks. Meanwhile, as exploratory research on the implementation of aligned interpretability, the following limitations are waiting to be resolved by further research:
Due its pipeline-specific nature, any changes the intermediate output format necessitate modifications in the design of the token mixer. For purely end-to-end models, such as black-box models, adjustments required handle latent outputs effectively.
The -baedd language decoder relatively time-consuming, Further investigation into smaller model alternatives, such as MiniChat-I.55 and StableLM-3B-4EITT warranted.
As LLM's potential to comprehend AD models' intermediate outputs becomes evident, future research can delve deeper into this area and enhance user trust in AD models through aligned interpretability.

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A Training and inference
The graphics memory usage for the language decoder varies with batch size, ranging from 3IGB (batch size 1) to 78GB (batch size = 24). For Hint- VAD, the memory usage ranges from 26GB (batch size = 1) to 45GB (batch size = 20). Training Hint- UniAD takes 13 hours for 3 epochs on A100 GPUs, while training Hint-VAI takes 7 hours on 8 A 100 GPUs. The inference times for the AD module and language decoder are detailed in Tab.
Table 5: Inference Time and Average Time per Batch. Evaluated on 1 single A100 GPU, this table presents the inference times and average time per batch for the AD model and language decoder of Hint- UniAD and Hint-VAr under different batch sizes of sentences to generate.
Method
Module
Batch=1
Batch=10
Batch=20
time
Avg. time/bacch
Avg. timehatch (S)
Avg. timehatch
Him-UniAD
All model
0.43
0.4
0.43
0.43
0.43
0.43
Language docoder
1.23
1.23
1,38
0.138
1.45
0.073
0.159
0:159
0.159
Hint-VAD
Language docoder
1.25
1:25
1.42
0.142
1.48
0.094
In Tab. 6, present the wall-clock time of the entire system with batch size of 1, as applied in real car scenario. This time encompasses data acquisition, pre-processing, and post-processing, all measured in seconds.
Data acquisition time includes capturing images from six HIKROBOT MV-CUO13-AOUC cameras and transmitting these images from the computer to the online server if using online inference.
Pre-proces pre-processing time involves preparing image sequences before inputting them into the model, which is conducted online if using online server.
Autonomous driving inference time refers to the time taken by UniAD and VAD to perform inferences.
LLM inference time calculated the time required to generate 26.5 tokens, which is the average generation length observed in research.
Post-processing time includes the duration needed for data transmission the inference conducted online.
Since users can access language output the first token is generated, define interactive latency the sum of data acquisition, pre-processing, autonomous driving inference, and post- processing times.
Table 6: Whole system latency measurements. Acq. refers to acquisition, AD refers to the inference time of AD model, Token gen. refers to the token generation speed, Inter. refers to interactive.
Methods
GPU
Data
Pre-process
LLM
Tooken gen.
Post-precess
Sum
Inter.
Hon-UloiAD
3050
1,85
14.5
2.32
0.49
Hin-UhiAD
coffline)
0.27
UniAD
(online)
1.24
21.5
(offline)
16.1
1.77
Hin--VAD
RTX 4090 (offinn)
<0.01
<0.01
0.07
0.59
45.2
<0.01
0.66
0.07
HiaE-VAL
VIDIA 1,00 tonline)
0.35
4100
0.16
1.25
21.3
0.04
1.79
0.54
B More on Model Designs
B.1 Parameters in Model Design
Here we provide Tab. 7 to specify parameters used our architecture and additional explanation.
BEV blocks consist of sequence of transformer encoder blocks. Each block includes normaliza- tion layer, multi-heat self-attention layer with 16 attention heads, second normalization layer, and multi- Mayte-layer perceptron (MLP) with hidden dimension of 3072.
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Instance blocks comprise a sequence of 5 transformer decoder blocks with the same internal structure as the BEV blocks. These blocks accept learnable tokens, Fiis-adapt and instance tokens, F intancee as input. A cross-attention mechanism is performed between these inputs, with Fins-adapt serving as the queries and Finstance serving as the keys and values.
Table 7: Notations.
Notation Shape & Params
description
Naon
layers front adapters inserted in a.M layers of end adapters inserted LLaMA
Nead
D Dembod
728
emheed dimension used in holistic token mixer
dimension of the BEV feature
Fbav
200
256
BEV tokens
Fuack
Nact x 256
Track tokens
Finotion
Nau x 256
Motion tokens
plon
Naxt x 256
Planning steps
Fiuetance
Nas x 256
Instance token
Esstance
pagg
256
Ego instance token
B.2 Comparison with Other Adaptation Methods
Here present analysis on why adapter is chosen as tuning method for Hint-AD.
We selected adapters due to considerations of training stability and parameter efficiency. As high- lighted by LLaMA- AdaMterr [59]. parameter-efficient fine-tuning methods like LoRA exhibit instability during training. particularly when addressing entirely new modalities. For instance, LoRA experi- ences gradient explosion within approximately 2.3K training steps. The LLaMA A-dapter addresses this issue by introducing zero-initialized cross-attention weights between adapter tokens and text queries.
We show experiment results with LoRA and DoRA in Tab. 8, demonstrating that the adapter is the only method that ensures stable training for this application while delivering superior overall performance.
Table 8: Comparison between adapter and LoRA on Hint-AD.
Params (%)
Stable training steps
No-X
Tap
NuSceme-QA
Cmd.
C
B-t
R1
Cr
B
M
R1
HD
HI
All
LoRA (==16)
0.672
2.3K
17.8
3.05
10.1
21.8
257.6
61.3
24.4
57.5
45.3
10.0
412
71.6
DoRA (r=16)
0,702
3.1K
18.3
3.72
1044
21.5
290
69.0
37.1
64.3
52.7
42.7
46.0
78.9
Adapter_epoch-J.e 3.1K
0.014
>20K
20,8
3,00
12.
26.5
307.3
68.9
43.3
75.6
52.4
45.5
47.8
75.6
B.3 Variations on Hint-VAD framework
VAD [21] uses the same BEV encoder UniAD [2), but vectorizes scene representations for planning and getting rid of dense maps. So the BEV lokens are the same: Frev E R XxXC where Ho. Wi, and C are the height, width, and channels of the BEV field. But for VAD has different motion prediction and planning phases, so the input tokens are adjusted as follows: In Track tokens ( 1N S IR. and Motion tokens {Fi [Fination) i= C R D. is the number of agent queries, not the number of detected objects. We select valid tokens based on the query's classification score, using threshold 0.5, thereby only considering queries with a high probability of corresponding to actual objects. Additionally, VAD considers both ego-agent interaction and ego-map interaction. We use updated queries as Ego token Fi C TR&,, which contain both dynamic and static information of the driving scene. Finally, we adopt Planning steps Fplan xT,x2 the future trajectories predicted by the model. We adjust the token mixer to accommodate the shape of each token while maintaining the same overall architecture.
N
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C More on experiments
C.1 More analysis on language-AD model alignment
To quantify the alignment between language and the intermediate outputs of the AD model, evaluate the output of language decoders against the results predicted by the AD perception modules, which is generated online on the validation set. We define four kinds of disalignment as follows:
Counting Disalignment (CD) measures the discrepancy between the number of instances of each category given by the text and the number detected by the AD model. It is defined as: CD
(6) V Nese Ei=r (Ctext (/ ) where Neat is the number object categories, Crext and AD are the counts of the ith category given by the text and the AD model, respectively.
Position Disaligument (PD) measures the difference in the position of a specific instance given by the text and the AD model. It is quantified by calculating the mean distance between the position in the caption Frext and the position predicted by the AD model TAD: PD Mcan Ftext FAD])-
Motion Disalignment (MD) measures the difference in velocity, calculated as the mean distance between the velocity in the caption Viext and the velocity predicted by the AD model VAD: MD Mean([Vred - YADI).
Planning Disaligument (PLD) measures the discrepancy in trajectory points. A trajectory is expressed
as four future positions of the ego car with 0.5s time steps between each [r(1), r()), (3) p p())) and
PLD is defined as the mean distance from the original AD prediction at 2s (L2 loss): PLD Mean([rtext AA
Here we offer a comparison of alignment between Hint-UniAD and various declarative alignment methods, as shown in Tab. 9.
Among the declarative methods, the Hint-UniAD architecture achieved the highest perfor- mance, except in the counting disalignment task, demonstrating its overall efficacy.
Methods utilizing pre-trained L.LM as language decoder performed poorly on the counting task. This poor performance arises from a bias towards certain numbers: for example, LLaMA tends to output specific integers like 10 and 20.
Table Comparison with other declarative alignment models. standardize the output formats of the language decoders used in these methods, we also tuned the models accordingly. N refers to no, and Y refers to yes.
Method
Alignment type
LLM
Counting
Position |
Motion
Planning
Hint-UniAD
aligned
Y
2.36
1.22
0.51
2.98
Hint-UniAD
declarative
5.82
10.01
1.98
10.12
ADAPT
declarative
N
8.30
14,23
2.49
13.07
BEVDe++MCAN
declarative
N
4.69
13.05
2.18
12.53
Vote2Cap-DETR
declarative
N
5.03
11.60
2.50
12.85
TO
declarative
Y
7.10
12.13
2.17
10.60
C.2 More Qualitative results
Here we demonstrate more detailed Qualitative results all four datasets. All predictions generated by Hint-UniAD model, and we choose TOD*Cap as baseline while evaluating results on Nu-X and Command datasets.
Our model excels in generating accurate and contextually appropriate predictions across multiple tasks. For driving explanation and command prediction, our model outperforms the baseline by providing more accurate narration, reasoning, and driving commands. In VQA tasks, it excels in accurately describing the surroundings, including the type and number of objects, In 3D dense captioning,
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model successfully detects surrounding objects and provides precise status descriptions. However, it occasionally make errors in identifying object colors and estimating distances. In alignment tasks, our model performs well across all directions but struggles with precise positional information due to limitations of image-based data. Additionally, it tends to undercount objects in counting tasks,
We also present
qualitative results from prompting GPT-4o and Gemini-1.5, shown in Figures 4 to 6. Hint-AD demonstrates better consistency with ground-truth annotations, achieving higher accuracy in VQA tasks such as determining whether an object is moving. In contrast, GPT-4o and Gemini-1.5 tend to provide more diverse descriptions of the scene and driving behavior. The full prompts are shown in Figure 8.
C.3 Analysis on testing difference between Hint-UniAD and Hint-VAD
According to Tab. 1, VAD performs much worse than UniAD on Tap dataset but slightly better on the other three datasets. This can be explained by VAD's restricted perception range 60m by 30m, whereas objects in TOD*Cap often fall outside this range. Since all questions TOD*Cap are object-centered, missed detections significantly lower the scores this dataset.
C.4 Baseline implementations
The original work [12] takes a sequence of raw video frames as inputs, and outputs natural language narration and reasoning. It employs Video Swin Transformer [52] as the video encoder. Two tasks, Driving Caption Generation (DCG) and Control Signal Prediction (CSP), jointly trained based on the same video tokens.
ADAPT. [12]
Here we adopt its text generation head, where the sentences are generated by vision-language transformer in an auto-regressive manner. The text inputs are tokenized and padded to fixed length, and then are embedded with a segment embedding method. While training, we use cross attention mask within text tokens and sparse attention mask between text tokens and BEV features. The vision-language transformer encoder starts with "[CLS]" token and generates word token at time, consuming previously generated tokens as the inputs until it outputs the ending token "[SEP]" or reaches the maximum length.
TOD*Cap [15] The original TOp framework is implemented using BEV detector module that processes both 2D and 2D+3D inputs. We adapted TOD*Cap for UniAD and VAD by adding an extra captioning head. The inputs for Image BEV features and object proposals are derived from the BEV tokens and tracking tokens of these two AD models, respectively.
Vote2Cap-DETR+ [46] Unlike the traditional "detect-theen-dssciiee" methods that first detect objects and then describe them, Vote2Cap-DETR++ uses transformer-bant framework to parallelly decode object localization and caption generation tasks. It uses 3D positional encoding is added to enhance the
the generated captions: Absolute Position Token is injected into the caption prefix to indicate the spatial location of the object 3D scene and Rank Bank-Based Position Encoding applied to local context tokens.
C.5 More Ablation Experiments
We have expanded the ablation studies to include multi-module ablation and cross-model consistency, encompassing both Hint-UniAD and Hint-VAD. The ablation results for Hint- UnitUnniAD are presented in Tab. 10 and Tab. 11. These results indicate consistent performance trend between Hint-UniAD and Hint- VAD. Notably, during further decline.
ablation, the performance of the model experiences
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CAM_ FRONT SERIES
Driving explanation
Command prediction
Baseline
Baseline
Narration:
The car continues straight ahead steady pace.
Reasoning: because the road ahead clear of any obstacles. Prediction
Forward and keep speed.
Prediction
Narration:
The car turns right at the intersection.
Turn right and keep speed.
Reasoning: enter the road on the right.
GPT-40
GPT-40
Turn right and keep speed.
Narration: The ego car gradually transitions the right Reasoning: prepare for right Gemini 1.5 Pro
the upcoming intersection..
Gemini 1.5 Pro
Turn right and keep speed.
Narration: The ego car is making right turn onto side street.
GT
Reasoning: the absence any other vehicles suggests the ego car not merely changing lanes instead navigating a turn.
right and keep speed.
G'I'
Narration:
The car cautiously navigates right turn. Reasoning: to enter the road on the right, maintaining safety guidelines.
CAM FRONT SERIES
Driving explanation
Command prediction
Baseline
Baseline
Narration:
The car stops at the intersection.
Reasoning: because the traffic light is red. Prediction
Forward and stationary.
Prediction
Narration:
The car stops at the intersection.
Forward and stationary.
Reasoning: to keep safe distance.
GPT-40
GPT-40
Forward and decrease speed.
Narration:
The car gradually decelerates and comes to complete stop.
Reasoning: due to the presence of large vehicle (likely bus or truck) directly ahead, which obstructing the path and necessitating halt avoid collision.
Gemini 1.5 Pro Forward and keep speed.
GT
Gemini 1.5
Forward and stationary.
Narration: The ego car is approaching a parked truck and slows down. Reasoning: The truck remains stationary across the frames, indicating it's parked As the ego car gets closer the truck, plausible speed navigate around safely.
slightly reduces
GT
Narration: The ego vehicle gradually reduce speed. Reasoning: to safely navigate around the parked truck.
Qualitative results on Nu-X and Command datasets. We choose TOD*Cap as baseline model and present GPT-4o and Gemini-1. -1. result.
Figure 4:
17

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CAM FRONT CAM_FRONT
LEFT
CAM FRONT CAM_FRONT
CAM FRONT RIGHT
BEV
T
CAM BACK LEFT
CAM BACK
CAM BACK RIGHT
KEPP TOWWAR
VQA
Question
Question
Question
There parked thing that is both the back of the stopped and the front left of me; what
pedestrian that to back right the stopped car in what status?
What number of other things in the status the truck?
Pred
car
Pred
moving standing standing
Pred
GPT--0
car
GPT-40
GPT-40
Gemini 1.5 Pro
car
Gemini 1.5 Pro
Gemini 1.5 Pro
GT
car
GT
moving
GT
Question
Question
Question
Are any parked cars visible?
The truck is in what status?
there any trucks the front me?
Pred
yes
Pred
stopped
Pred
yes
GPT-40
yes
GPT'-40
stopped
GPT-40
yes
Gemini 1.5 Pro
yes
Gemini 1.5 Pro
parked
Gemini 1.5 Pro
no
yes GT
stopped
GT
yes
Figure 5: Qualitative results on NuSence- QA with GPT-4o and Gemini-15 outputs.
Table 10: More ablation on holistic alignment
Nu-X
Cop
NuScenes-QQ
Cmd.
Method
Ablated
C
B f
M
R1
Ct
B
M
H01
f
Acc.
19.5
2.95
11.3
120.3
48.0
53.0
42.6
46.1
82.5
motion
22.3
3.02
12.6
26.5
267.3
67.2
43.0
81.3
53.6
43.5
46.9
82.4
planning
20.8
3.10
11.4
23.5
290.6
68.4
44.5
85.6
55.3
46.7
49.6
79.1
track planning
2.98
60.4
53.4
80.6
Hint-UniAD
motion & planning
21.0
3.06
12.1
25.2
280.7
66.1
44.3
78.1
54.6
45.1
48.2
80.7
truck & motion
20.1
3.03
11.9
24.1
54.1
44.2
47.5
81.2
3.47
81.1
none
21.7
4.20
12.7
27.0
342.6
71.9
48.0
85.4
56.2
47.5
50.4
83,0
track
20.0
3.03
11.4
22.0
132.6
44.5
46.9
56.8
52.6
46.5
48.9
83,9
Hint-VAD
motion
19.5
2.96
11.0
21.1
233.5
59.8
44.2
71.6
54.1
49.2
50.9
81.2
planning
18.8
2.86
246.7
66.8
53.9
77.8
none
22.4
4.18
13.2
27.6
263.7
67.6
47.5
79.4
55.4
48.0
50.5
82.3
Table 11: More ablation on holistic token mixer.
Nu-X
TOD
NuScenes-
Cmd.
Method
Ablated
C
B
M
Ct
M 1
H0
HI
All
Acc.
instance mixer
20.8
3.22
10.6
26.5
220.4
58.3
46.9
73.0
$2.0
44.8
47.2
81.6
instance blocks
21.3
3.24
11.3
27.1
259,4
62.5
47.0
82.3
$4.3
47.2
49.6
81.3
Hint-UniAD
all
19.1
3.02
11.1
25.1
164.3
55.1
45.1
70.2
51.1
44.1
46.4
82.1
none
21.7
4.20
12.7
27.0
342.6
48.0
85.4
56.2
47.5
50.4
83,0
instance mixer
21.6
4.09
12.4
189,4
43.8
53.0
45.1
47.7
80.7
Hint-VAD
instance blocks
22.0
4.17
13.3
27.3
249.8
57.9
45.7
74.5
53.6
46.1
48.6
81.0
none
22.4
4.18
13.2
27.6
263.7
67.6
47.5
79.4
55.4
48.0
50,5
82,3
18

---

CAM_FRONT LEFT
FRONT RIGHT
CAM BACK RIGHT
CAM_BACK
CAM BACK LEFT
3D dense captioning
Object
Object
Pred
Pred
black, shiny, and sleek about 13 meters away the back ego moving quickly the driving lane.
person wearing shirt blue jeans about meters away in the back left ego car moving slowly the walkway.
GPT-40
GPT-40
Gray car about 20 meters away back ego car is moving slowly in the driving lane.
person wearing dark clothing about 1o meters away the back of the ego car walking on sidewalk.
Gemini 1.5 Pro
Gemini 1.5 Pro
car about 15 meters away in the back of ego car is moving slowly in the driving lane.
Person about meters away in the back left of ego moving slowly in the walkway.
GT
GT
black, shiny, sleek car about 13 meters away the back ego car moving quickly the intersection.
person wearing purple shirt and black jeans about 18 meters away in the back left ego car moving slowly in the walkway.
Object Pred
Object
Pred
black, shiny, and sleek car about meters away in the back left of ego is moving.
person wearing black shirt and black jeans about meters away in the front of ego moving slowly.
GPT-40
GPT-40
A person wearing dark clothing about to meters away in the back left ego car walking on the sidewalk.
A person wearing dark clothing about 20 meters away front the ego car stationary on sidewalk.
Gemini Pro
Gemini 1.5 Pro
about away in the front left of not moving the driving lane.
car about meters in the back of ego car moving slowly in the driving lane.
GT
GT
wearing white shirt and black pants about meters away in the front of ego moving slowly.
white, silver, and black sleek about 27 meters away the back left ego moving.
Figure 6: Qualitative results on TOD*Cap with GPT-4o and Gemini-1.5 outputs.
19

---

CAM FRONT LEFT
GAMLFRONT
CAM FRONT RIGHT
BEV
CAM BACK RIGHT
CACK BACK
CAM_BACK LEFT
GALDSOO
Alignment tasks
Type Counting
Type Position
Type Motion
Type Planning
Pred
Object
Object
Pred
There are car truck construction, vehicle barrier detected this frame.
Pred
Pred
The trajectory points are [[0.12.899.1000355799. [0.058..65].0000.11.5s]]]
It's about 10.1 meters away at
The object is moving at 6.3
11 o'clock direction.
m/s o'clock direction.
GT
GT
GT
It's about 9-9 meters away at 11 o'clock direction.
The trajectory points are [[0.053:7].009996666.... 9.10.89).
There car truck a motorcycle 1 traffic_cone detected frame.
The object is moving 5.98 m/s its o'clock direction.
Figure Qualitative results on alignment dataset.
C.6 Caption evaluation
GPT-3.5 for Logicality and Accuracy Assessment The evaluation of visual language models for automated driving presented in this paper focuses on two main components: the logicality assessment of driving descriptions and the accuracy evaluation of driving behavior predictions.
For the logicality assessment of driving descriptions, utilized GPT-3.5 to determine the correlation between driving behavior descriptions and the triggers for those behaviors in the model's predictions. Initially, we prompted GPT-3.5 to extract key information from each predicted description, including the car's ' driving behavior (movement and geographic location) and the causative factors (subject and form). We evaluated the associative relationship between the preceding and following events based on three criteria:
The consistency between the car action implied by the causative factors and the predicted driving behavior.
The logical coherence between the causative factors and the predicted driving behavior.
The consistency between the details in the causative factors and those in the driving behavior.
Each metric was scored and summarized for each component.
Additionally, we conducted a comparative analysis of the similarity between predicted and manually labeled driving behaviors. Using GPT-3355, we extracted key information, such as the car's action, purpose, and reason, from both the predicted and manually labeled driving behaviors for comparison. Each pair was scored based on the degree of match (complete, partial, or missing). The primary metrics for this comparison included:
The similarity between driving behavior and geographic location.
The objects and reasons leading to such driving behavior.
Other relevant details.
20

---

System prompt
expert system in autonomous driving. Help with these perception, prediction and planning tasks.
Input prompt
These are six photos taken from cameras positioned at the front, front front-right, right, front left, back, back left, and back right the vehicle.
With picture urls of six photos of different views
or
These five photos taken cameras positioned front
With picture urls of five front view photos, depending on the task.
Followings several questions. Read the images above and the questions one by
Nu-X
Describe the bchavior of the ego and the reason behind
Example 1: Narration:
the ego car smoothly transitions into the right lane on bustling city street.
Example Narration: the purpose aligning itself with traffic flow entering the right lane.
Reasoning:
merging into the left lane.
Reasoning: because the lane is moving faster.
Example 3 Narration:
The ego vehicle gradually reduce its speed. to safely navigate around the parked truck.
Reasoning:
Example 4: Narration:
cautiously decelerates Reasoning: due the fact that the intersection lying ahead governed by halt-inducing red light.
complete stop.
Example 5 Narration: the car gradually decelerates, coming to complete halt.
Reasoning due
presence of red traffic light the upcoming intersection.
Example 6 the car meticulously maneuvers to the right lane.
Reasoning: so to merge into the right lane due to upcoming
TOD*
Describe the given object in the format of <attribute>> about <distance> meters away <localization> <motion> <map.. Example : black, shiny, and sleck car about 25 meters away in the back of ego car moving quickly in the driving lane.
Example person wearing white shirt black jeans about 26
away the back ego car standing.
Example yellow bicycle about 46 meters away in the front of ego car not moving. Example : white, sülver, and gray car about 22 meters away in the front
ego car not moving. Example 5: person wearing black shirt and black pants about 56 meters away in the front of ego is moving slowly the walkway.
Example trafflccone about
away the back of
moving driving
For NuScenes: QA
Answer each questions only in single word or number, with out any extra output, output.Seperate. Seperate answers with comma. Your output should be like' "s..yee..o.no.no.ce where each answer corresponds to question. Example moving
Example 2 without rider.
Example 3 pedestrian.
Example 4 yes.
Example 5
Example 6 bicycle.
For Command
Pleuse predict the control signal of the ego car. Choose from: "Turn right, 'Turn left', and 'Forward.
Then predict motion. Only give the final control signal in
Example 1 FForward and keep speed. Example 2 Forward and stationary.
Example 3: Forward and increase speed.
Example 4 Forward and decrense speed.
Example Turn right and keep speed.
Example 6: Turn left and keep speed.
Figure 8: Prompts for GPT-40 and Gemini-1.5.
21

---

Penalties were applied for instances of fictitious information, which reduced the total score. The mean these scores constituted the scene description score. The total prompt for GPT-3.5 is shown in Fig.
System prompt
You are smart and strict logical evaluator with extensive driving experience, and your best give reasonable and logical evaluation result.
Input prompt
Analysis adaplations of oulpul, the main scoring principles are:
Consistency between the driving behavior and reasoning of output (o, max:5).
2. Coherence and logic between the driving behavior and reasoning of output (o, max:5).
3. Consistency of detail between the behavior and reasoning of output (o, max:5).
4. Driving behavior and location (o, 5, 10, 15, max:2o).
5. What and why cause driving behavior (o, 5, 10, max:15). details 5, max:1)..
7. Presence irrelevant information (-5, maxco).
You need provide the rigorously evaluated for each of the criteria. Ensure the fairness of the output results.
Response
Info x Evaluation: Itemi: Item:: Item3:
Item4:
15 Items: 15 Item6: Itemy:
Final Score: 8.3
Figure 9: Prompt for GPT-3.5 on semantic evaluation
D
More on Nu-X
Nu-X dataset
Source dataset
muScemess
Type
Explanation
Condition
Urban
Lighting
Day / Night
Narration: the
Narration the gently reduces speed and maneuvers right turn, Reasoning: since the vehicle front abiding by the traffic light and the pathway curving the right.
Narration the decelerates and cantiously approaches the left turning lane, eventually coming hall,
Narrations remains stationary the street intersection, Reasoning to comply with the red traffic light signal, preventing any movement for safety
Hours
continues journey straight line,
Videos
#5o
Reasoning maintaining distance from vehicle ahead, oheying the double yellow line
Frames
341-49
Captions
34149
Reasoning: to yield right way pedestrians
Training
28130
prevent a potential accident,
crossing the street ahrad
Val. Testing
6019
Avg. captions / videos
40.2
Figure 10: Examples of Nu-X dataset.
D.1
Annotation procedure
To balance accuracy and effectiveness of annotation, we conducted process incorporating both human labelling and LLM diversification (see Fig. 11). The training / validation branches of nuScenes contain 850 videos, each spanning about 20 seconds, with approximately 40 key frames per video annotated. labelling all frames is inefficient driving behaviors and road conditions in adjacent frames are often similar. Each video typically features only 3-4 distinct driving behaviors. However, identical annotations across frames risk over over-fitting, reducing effectiveness. Therefore, we used MILLM diversify expressions and add scene-description details each frame. The annotation process includes the following procedures:
Human labelling We employed five professional annotators for a total of 126 hours. All annotators familiar with US driving rules and have driving experience. They were instructed describe specific driving behaviors and the potential reasons for these decisions. Annotations for each video include the time intervals of certain driving behaviors, along with narration and reasoning.
22

---

(a)
Video keyframe
Human labelling
GPT-4v image description
GPT-4vaatto diversification
Human check
(b)
(c)
Count
Count
12,000
10,000
10,000
8,000
6,000
6,000
2,000
2,000
Narration
Reasoning
10
Length
Length
(d)
without
accelerates
ahead
allowing
velicle
aleay
vacant
cantiously
mobsstraced
Tarn
clear
truffic
crosseralk
street
straight
drives
slop
ensoring
sleudy
steadily
forward
stationary
speed
gradually
space
halt
smoothly
slows
junction
safe
lane
molte
rond
light
sight
remains
moves
reduces
red
obstructions
procedss
pedestrians
Figure 11: Annotation and data statistics of Nu-X. (a) The annotation process of Nu-X dataset; (b)
Distribution of the sequence length of narration; (c) Distribution of reasoning sentence length; (d) Distribution of different vocabulary in Nu-X.
23

---

For narration, annotators focused on at least 11 driving behaviors, such as directional decisions (e.g.. go forward, turn left, turn right, U-turn) and velocity commands (e.g., accelerate, decelerate, stop, stay stationary). Descriptors like "slightly" or "sharply" were encouraged.
For reasoning, annotators incorporated specifie road conditions, key instances (e.g., pedestrians, front cars), and traffic rules (e.g.. traffic lights, stop signs, yellow lines).
GPT-4v image description Before diversification process. we firstly leverage GPT-4v to generate the image description of multi-view camera images to extract the visual information. Specifically, we provide GPT-4y with each frame of multi-view images in nuScenes and task it with capturing and recording key traffic information from these images. This information includes, but is not limited to, pedestrians, vehicles, traffic signals, and other relevant road signs. Through this process, GPT-4v is able to automatically extract and provide detailed descriptions of the elements and dynamics present in each image frame in sentence of about 120 word length.
With the image description, we utilize GPT-4v to automatically generate diverse outputs based on the human-labeled captions, which is particularly useful for creating varied descriptions and perspectives of the same set of images. We also constraints the text output follow the original main driving behavior and reasoning of human annotators to ensure the correctness.
GPT-4v auto-diversification
Human check We conduct thorough human check ensure accuracy and relevance after the auto-diversification. This step involves experts reviewing and validating the multiple interpretations generated by GPT-4v, verifying that the descriptions accurately reflect the content of the images. The human check process is essential for maintaining high standards of quality and reliability in our traffic analysis, ensuring that the diversified outputs align with real-world observations.
D.2 More on data statistics
We show some key results of data statistics in Fig. 11(b)-(d). The average length of a narration is 8.29 words for sentence, and the average length of reasoning is 11.25 words.
E
More on command dataset
The ground truth of the nuScenes dataset provides only three types of directional commands: <TURN LEFT>, <TURN RIGHT>, and <FORWARD>>, which lack the necessary velocity information. To address this, we labeled command dataset using programmed protocols based on the ground truth future steps, incorporating additional velocity commands such as <ACCELERATE> <DECELER- ATE>, <STATIONARYS, and <KEEP SPEED>.
Directional commands generated by analyzing the trajectory slope, applying criterion where driving 5 meters with only meter of lateral drift was considered.
For velocity commands, polynomial fitting was used to smooth the speed curve derived from the average speed between predicted points. The average acceleration over the next seconds was then used determine the velocity commands. Given the vehicle's increased sensitivity to speed changes when approaching zero, different thresholds for speed changes were set: 0.36 m/s? 0.12 m/s? and 0.05 m/s² for initial speeds of >2 m/s, 1-2 m/s, and <1 m/s, respectively.
24


Reviews:
[{"review_id": "oWU5eCdoI7", "reviewer": "Reviewer_HNsL", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "The paper introduces a holistically aligned AD-language system designed to generate explanations for driving behaviors. Specifically, the authors demonstrate that by integrating tokens and employing an alignment procedure across all components of the holistic perception-prediction-planning stack, the LLM model enhances performance in driving language tasks. The effectiveness of this approach is validated on three distinct datasets, including the newly introduced Nu-x dataset, which provides driving explanations aligned with frames from the widely used nuScenes dataset.", "summary_of_recommendation": "Interesting work, decent experimental session with good ablation studies. But the core of the approach brings questions about future impact of this work. Hence I propose \"weak accept\"", "technical_quality": 4, "clarity_of_presentation": 3, "potential_impact": 3, "robotics_focus": 3, "quality_of_limitations": 2, "questions_for_rebuttal": "Please, see the review section.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Do8Y7zcMvb", "reviewer": "Reviewer_Kmwf", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "In this paper, the authors introduce a novel approach called \"aligned interpretability\" for autonomous vehicle (AV) actions. This algorithm/method aims to provide intermediate interpretability to the AV decision-making processes by aligning language generation with the intermediate outputs of the AD model.  Authors have developed a new interpretability method, introduced a new dataset (Nu-X), and created a framework (HINT-AD) that generates language aligned with the perception-prediction-planning outputs of the AD model.", "summary_of_recommendation": "I recommend a weak accept for this paper. The work presents a novel and potential impactful contribution to the field of interpretable AI with the introduction of the HINT-AD framework and the concept of aligned interpretability. The paper demonstrates clear methodological rigor and fair experimental validation, showing some improvements over baseline models across multiple language tasks. The creation of the Nu-X dataset is also a valuable contribution to the research community and will help advance the field. However, the paper has some limitations that prevent a stronger recommendation. These include the 1. Need for more detailed technical explanations in some areas, 2. A lack of comparison with state-of-the-art multi-modal language models, and 3. Insufficient discussion of real-world applicability such as latency. Despite these shortcomings, the  approach and potential impact of this work make it worthy of inclusion in the conference, with the expectation that the authors will address these issues in the final version or in future work.", "technical_quality": 2, "clarity_of_presentation": 4, "potential_impact": 3, "robotics_focus": 3, "quality_of_limitations": 1, "questions_for_rebuttal": "- What is the rationale for not passing planning and ego motion through an attention layer similar to the BEV and instance blocks?\n- Explain the why not perform a SFT instead of adaptor, give the task performed is different compared to typical training data in the LLM ?\n- Given that MLLM adapter training (e.g., LoRA) is shown to preserve pretrained information and language understanding, why weren't low-rank adapters used instead of full adapters?\n- It is highly recommended to include comparisons with multi-modal language models such as GPT-4, LLAVA-v1.6, and Gemini 1.5 for VQA and other tasks. This comparison would help quantify the improvement over out-of-the-box models, especially given that LLAVA is the base MLLM for HINT-AD.\n- The declarative alignment results of the HINT-AD models proposed here should be compared with other declarative alignment models. This could highlight whether HINT-AD is increasing overall efficacy of the alignment or specifically improving the efficiency of aligned interpretability.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pssoCcbI4T", "reviewer": "Reviewer_kWqV", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 4, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "In this paper the authors define a distinction between \"declarative\" and \"aligned\" interpretability, arguing that aligned interpretability, where language explanations are grounded in the internal state of a system, is necessary to explain _why_ certain decisions are made. This motivates the authors' development of the Hint-AD framework which provides aligned interpretability for modular perception-prediction-planning autonomous driving stacks (specifically UniAD and VAD in this work), and moreover results in improved performance on various vision-language driving tasks including a new human-labeled driving explanation dataset + task Nu-X. These advances are enabled by a new AD-language model architecture that incorporates intermediate module outputs and is trained on various online alignment tasks to encourage the learning of token representations well-adapted to interpretable reasoning.", "summary_of_recommendation": "I recommend this paper's acceptance at CoRL 2024 as it both presents strong benchmark results/corresponding insights for AD practitioners, in addition to addressing a topic of broader interest/importance in the robot learning community today: the importance of alignment in interpretable AI. However, as noted above, I believe the paper would be improved if the limitations of this work could be more clearly established.", "technical_quality": 4, "clarity_of_presentation": 4, "potential_impact": 3, "robotics_focus": 3, "quality_of_limitations": 2, "questions_for_rebuttal": "From the comments/questions above, please prioritize:\n- Is greater model/data scale all that is needed to drive disalignment down to 0?\n- Could aligned interpretability be achieved for black-box models?", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WBfO2COyFo", "reviewer": "Reviewer_rKXi", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "The paper presents a systems level approach to improving interpretability in Autonomous Driving (AD) systems. The authors argue for an aligned interpretability approach as opposed to declarative interpretability in order to improve interpretability of the decision making process within AD systems. Specifically, they propose the use of intermediate outputs (from perception, prediction and planning) in the AD stack to provide contextual information to a foundation model to achieve this goal. They propose the use of features extracted from multi-view images as well as track, motion, and planning information obtained from the rest of the AD stack and present a method to efficiently and effectively combine this contextual information towards generating better aligned responses. Lastly, they created a dataset by augmenting the nuScenes dataset with detailed contextual information.", "summary_of_recommendation": "The authors have presented their approach and its motivation with clarity and have extensively evaluated it both quantitatively against other methods, across several metrics and benchmarks as well as qualitatively. The paper, however, would benefit from clarifying claims made around generality of the approach and from providing additional information around the limitations of the system. That being said, the proposed approach provides sufficient contributions for me to recommend a Weak Accept.", "technical_quality": 4, "clarity_of_presentation": 3, "potential_impact": 3, "robotics_focus": 3, "quality_of_limitations": 3, "questions_for_rebuttal": "1. What is the latency of the proposed approach as a whole i.e., of the augmented AD system?\n2. What are the limitations of the proposed approach?\n3. How does the success of the proposed approach on UniAD and VAD demonstrate the methods' generality? (Claim made in the paper)", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Zheew9ZfDM", "reviewer": "Reviewer_dfFt", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "Multi-modal language model for aligned interpretability in autonomous driving", "summary_of_recommendation": "Good extension of 3D-LLM s to dynamic scenes in autonomous driving as well as the contribution of a useful dataset", "technical_quality": 4, "clarity_of_presentation": 4, "potential_impact": 3, "robotics_focus": 4, "quality_of_limitations": 3, "questions_for_rebuttal": "The experiment section could be made stronger:\n\n1. Baselines in table 1 could be extended. For example, although many closed-source VLMs do not support finetuning, they could still be used as baseline including GPT-4V, Gemini-1.5. Other 3D-LLMs that are good at spatial metric prediction including SpatialRGPT, Spatial GPT, Transcrib3D can also be considered adding as baselines.\n\n2. While table 4 investigate the effectiveness of \"Barbell adaption\" against other approaches in terms of layer-choosing strategies, it would also be helpful evaluate against other adaption strategies beyond layer-choosing, for example, LoRA, DoRA, IA3.\n\n**Update after rebuttal:**\nThank authors for the additional experiments. I believe they made the paper stronger. I have raised my rating on technical quality.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OZ0H05ZUXU", "reviewer": "Reviewer_3Cfu", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "This paper proposes a mixer sub-network to align the intermediate outputs from a BEV perception-prediction-planning system for autonomous driving (AD) with a multimodal LLM (MLLM) for driving explanation, 3D dense captioning, and command prediction.  The contribution of this work is to enhance interpretability in end-to-end AD, achieving better performance than the declarative interpretability that is not grounded in the intermediate outputs of AD systems.", "summary_of_recommendation": "The paper shows some potentials for interpretable autonomous driving using multimodal LLM, but the lack of detailed insights of the model's design and evaluation result in a week rejection.", "technical_quality": 2, "clarity_of_presentation": 2, "potential_impact": 2, "robotics_focus": 3, "quality_of_limitations": 2, "questions_for_rebuttal": "A more comprehensive ablation study is needed to validate the model's performance.\n\nA better definition of interpretability is helpful to understand the contribution of the proposed approach.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: Strengths:
1. Contributes a dataset (nu-X) for explainability in autonomous driving 
2. Extensive quantitative and qualitative evaluations
3. Modular approach to interpretability, a key safety research area

Weaknesses:
1. Modularity for the sake of interpretability may not survive the larger trend of end to end methods taking over. Future value of this work is contentious.
2. Please cover limitations in more detail:
- how well does the method generalize
- lack of enforced alignment
- comparison against multimodal LLMs 
- latency of system for driving


Reviewer HNsL:The reviewer questioned the future value of the modular approach in Hint-AD, given the trend towards fully end-to-end methods in autonomous driving. They also pointed out the system's dependence on specific token formats, which could limit its applicability. The authors clarified that Hint-AD is already built on end-to-end AD systems and that incorporating intermediate outputs is a common practice in such systems. They also highlighted that Hint-AD is not dependent on specific token formats and can work with various types, given sufficient data and a well-designed encoder.

Reviewer Kmwf: The reviewer raised several concerns, including a lack of technical details in certain areas, the absence of comparison with state-of-the-art multi-modal language models, and insufficient discussion of real-world applicability, such as latency. The authors provided more detailed technical explanations, included comparisons with GPT-4 and Gemini 1.5, and added a discussion on real-world applicability, focusing on latency and inference speed.

Reviewer kWqV: The reviewer questioned the limitations of Hint-AD, particularly regarding lingering disalignment and the applicability to non-modular, black-box end-to-end AD systems. The authors discussed the possibility of reducing disalignment through strategic model design and larger data scales. They also addressed the applicability to black-box models, explaining that aligned interpretability could be achieved by passing BEV perception and inner states to the MLLM.

Reviewer dfFt: The reviewer suggested extending the baselines in the experiments and evaluating other adaptation strategies beyond layer-choosing. The authors included additional experimental results featuring GPT-4, Gemini 1.5, LoRA, and DoRA. They also explained why most 3D-LLM baselines are incompatible with their use case.

Reviewer 3Cfu: The reviewer pointed out a lack of clarity in the method presentation, the ablation studies, and the definition of interpretability. The authors provided a more detailed figure, clarified the loss functions and training stages, expanded the ablation studies, and provided a clearer definition of interpretability and how it's measured.
Decision: Accept