Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
Authors: Hao Shao
Letian Wang
Ruobing Chen
Hongsheng Li
Yu Liu
Venue: CoRL 2022 Poster (2022)
Keywords: autonomous driving
sensor fusion
transformer
safety

Abstract: Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of which would result in vulnerability to rare but complex traffic situations, such as the sudden emergence of unknown objects. However, reasoning from a global context requires access to sensors of multiple types and adequate fusion of multi-modal sensor signals, which is difficult to achieve. On the other hand, the lack of interpretability in learning models also hampers the safety with unverifiable failure causes. In this paper, we propose a safety-enhanced autonomous driving framework, named Interpretable Sensor Fusion Transformer (InterFuser), to fully process and fuse information from multi-modal multi-view sensors for achieving comprehensive scene understanding and adversarial event detection. Besides, intermediate interpretable features are generated from our framework, which provide more semantics and are exploited to better constrain actions to be within the safe sets. We conducted extensive experiments on CARLA benchmarks, where our model outperforms prior methods, ranking the first on the public CARLA Leaderboard.

Safety-Enhances Autonomous Driving Using Interpretable Sensor Fusion Transformer
Hao Shao!* Letian Wang?* 2* Ruobing Chen
Hongsheng Li Liu
SensseTime Research
University of Toronto
3 "The Chinese University of Hong Kong
Shanghai Artificial Intelligence Laboratory
Abstract: Large-scale deployment of autonomous vehicles has been continually delayed due safety concerns. On the one hand, comprehensive scene under- standing is indispensable, a lack of which would result in vulnerability in rare but complex traffic situations, such the sudden emergence of unknown objects. However, reasoning from a global context requires access to sensors of multiple types and adequate fusion of these multi-modal sensor signals, which difficult to achieve. On the other hand, the lack of interpretability in learning models also hampers the safety with unverifiable failure causes. In this paper, we propose a safety-enhanced autonomous driving framework, named Interpretable Sensor Fusion Transformer (InterFuser), to fully process and fuse information from multi- modal multi-view sensors for comprehensive scene understanding and adversarial event detection. Besides, intermediate interpretable features are generated from our framework, which provide more semantics and are exploited to better constrain actions to within the safe sets. We conducted extensive experiments on CARLA benchmarks, where our model outperforms prior methods, ranking #1 on the public CARLA Leaderboard. Code made available to facilitate further research.
Keywords:
Autonomous driving, sensor fusion, transformer, safety
Introduction
Recently, rapid progress has been witnessed in the field of autonomous driving, while the scalable and practical deployment of autonomous vehicles on public roads is still far from feasible. Their incompetence mainly observed in bigh-traffie-densip scenes, where large number of obstacles and dynamic objects are involved in the decision making, In these cases, currently deployed systems could exhibit incorrect or unexpected behaviours leading to catastrophic accidents [1, 2]. While many factors contribute to such safety concerns, two of the major challenges are: 1) how to recognize rare adverse events of long-tail tail distributions, such as the sudden emergence of pedestrians from road sides, and vehicles running red light, which require a better understanding of the scenes with multi-moda multi-view sensor inputs; 2) how to verify the decision-making process, other words, identify dunctioningmalfunccionnng conditions of the system and the causes for failures, which requires interpretability of the decision- decision-making system.
Safe and reliable driving necessitates comprehensive scene understanding. However, single sensor generally cannot provide adequate information for perceiving the driving scenes. Single-image approaches can hardly capture the surrounding environment from multiple perspectives and cannot provide 3D measurements of the scene, while single-LiDAR approaches cannot capture semantic information such as traffic lights. Though there are existing works fusing information from multiple sensors, they either match geometric features between image space and LiDAR projection space by locality assumption [3, 4], or simply concatenate multi-sensor features [5, 6]. The interactions and relationships between multi-modal features are seldomly modeled, such as the interactions between
"Equal contribution CCoresponding author
6th Conference on Robot Learning (CoRI. 2022), Auckland, New Zealand.

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multiple dynamic agents and traffic lights, or features in different views and modalities. To encourage reasoning with a global context, the attention mechanism of Transformer [7] is utilized. The recent TransFuser [8] adopts internal feature propagation and aggregation via a multi-stage CNN-transformer architecture fuse bi-modal inputs, which harms sensor scalability and is limited to fusion between LiDAR and a single-view image. In this paper, we take one-stage architecture to effectively fuse information from multi-modal multi-view sensors and achieve significant improvement. As shown in Fig. 1, we consider LiDAR input and multi-view images (left, front, right, and focus) as complementary inputs to achieve comprehensive full-scene understanding.
On the other hand, existing end-to-end driving methods barely have safety ensurance mech- anism due to the lack of interpretability of how the control signal generated. To tackle such challenge, there are efforts to verify the func- tioning conditions of neural networks instead of directly understanding the models [9, 10, II]. Though being helpful for choosing different models for different conditions, these methods still lack feedback from the failure causes for further improvement. Inspired by humans' in- formation collecting process [12], in addition to generating actions, our model also outputs intermediate interpretable features, which we call safety mind map. As shown in Fig. 1, the safety mind map provides information on sur- rounding objects and traffic signs. Unveiling the process of perceiving and decision-making, our model is improvable with clear failure con- ditions and causes. Moreover, by exploiting this intermediate interpretable information as a safety constraint heuristic, we can constrain the actions within safe action set to further enhance driving safety.
Left View
View
Right View
Attention
Safety Mind Map
Goal Location
Figure I: Safe and efficient driving requires comprehen- sive scene understanding by fusing information from multiple sensors. Peeking into the intermediate inter- pretable features of learning models can also unveil the model's decision basis. Such features enable improvable systems with access to failure causes, can be used as safety heuristic to constrain actions within the safe set.
In this paper, we propose safety-enhanced driving framework called Interpretable Sensor Fusion Transformer (InterFuser), in which information from multi-modal multi-view sensors is fused, and driving safety is enhanced by providing intermediate interpretable features as safety constraint heuristics. Our contributions are three-fold:
1. We propose novel Interpretable Sensor Fusion Transformer (InterFuser) encourage global contextual perception and reasoning in different modalities and views.
2. Our framework enhances the safety and interpretability of end-to-end driving by outputting intermediate features of the model and constraining actions within safe sets.
3. We experimentally validated our method on several CARLA benchmarks with complex and ad adversarial urban scenarios. Our model outperformed all prior methods, ranking the first on the public CARLA Leaderboard.
2 Related work
End-to-end autonomous driving in urban scenarios The research of end-to-end autonomous driving based on the simulator of urban scenarios has become more and more popular. The topic starts from the development of an urban driving simulator: CARLA [13], together with Conditional Imitation Learning (CIL) [3]. A series of work follow this way, yielding conditional end-to-end driving [14, 15] in urban scenarios. LBC [16] proposed the mimicking methods aimed at training image-inpu networks with the supervision of a privileged model or squeezed model. Chitta et al. [17] presents neural attention fields to enable the reasoning for end-to-end driving models. Imitation learning(II.) approaches lack interpretability and their performance is limited by their handcrafted expert autopilot. Hence, researchers develop Reinforcement Learning (RI. agents to interact with simulated environments. Latent DRL [18] generates intermediate feature embedding from top-

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Safety Controller
Obventora
Torto
Jicuer Errvodiing
Constraint
Transformer
Transformer Decoder
Encoder
Stos
Figure 2: Overview of our approach. We first use CNN backbones to extract features from multi-modal multi- view sensor inputs. The tokens from different sensors are then fused in the transformer encoder. Three types of queries are then fod into the transformer decoder to predict waypoints, object density maps and traffic rules respectively. At last, by recovering the traffic scene from the predicted object density map and utilizing the tracker to forecast the future motion of other objects, safety controller is applied to enhance the safety and efficiency of driving in complex traffic situations.
down view image by training variational auto-encoder. With the aforementioned mimicking tricks, Roach [19] trained an RL-based privileged model as the expert agent to provide demonstrations for the IL agent. Toromanoff et al. [6] proposes to use hidden states which is supervised by semantic information as the input of RL policy.
Transformer model in vision comprehension Transformer was originally established in Natural Language Processing (NLP) [7]. The attention mechanism demonstrates to be a powerful module in image processing tasks. Vision Transformer (ViT) [20] computes relationships among pixel blocks with a reasonable computation cost. Later generations move on to generalize Transformer to other computer vision tasks [21, 22, 23, 24] digging deeper perform better [25, 26]. The attention mechanism brings a new entry point for modality fusion. TransformerFusion [27] reconstruct 3D scenes that takes monocular video input with transformer architecture. TransFuser [8] exploits several transformer modules for the fusion of intermediate features front view and LiDAR. However, such sensor-pair intense fusion approach hardly scales more sensors, while information from side views like an emerging pedestrian, and a focus view like the traffic light, are critical for scene understanding and safe driving.
Safe and interpretable driving Safety has long been studied the traditional planning/control community. However, in autonomous driving, uncertain behaviors [28, 29, 30), diverse driving preferences of drivers [31], numerous driving situations [32] deteriorate the safety concern. Tradi- tional rule-based methods usually hand-crafted different delicate rules to tackle different driving situations [32]. However, such hand-crafted designs usually require heavy human enginecring effort methods aims at
and it is hard to enumerate all possible cases. In comparison, learning-bact learning diverse driving behaviors from data without heavy human design labor. However, their lack of interpretability becomes new puzzle in the way. There efforts taking a bypass, verifying the functioning conditions of neural network models instead of directly understanding them [9, 10. 11]. However, feedback on the failure causes and solutions are still wanted. Some works design auxiliary tasks to output interpretable semantic features [33, 34, 35], which is showing a great improvement on both the performance and interpretabilityy In this paper, output the intermediate interpretable features for more semantics, which are then used in downstream controller to explicitly enhance driving safety.
3 Method
As illustrated in Fig. 2, the proposed framework consists of three main components: 1) a multi-view multi-modal fusion transformer encoder that integrates the signals from multiple RGB cameras and LiDAR; 2) transformer decoder generating the low-level actions, and interpretable intermediate features including the ego vehicle's future trajectory, the object density map, and the traffic rule signals; 3) safety controller which utilizes the interpretable intermediate features to constrain the
3

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low-level control within the safe set. This section will introduce the input/output representation. model architecture, and safety controller.
3.1 Input and Output Representations
Input representations Four sensors are utilized, including three RGB cameras (left, front and right) and one LiDAR sensor. Four image inputs are obtained from the three cameras. In addition to the left, front and right image input [hett Iront, J.ight. we also design focusing-view image input Irouus to capture the status of distant traffic lights by cropping the center patch of the raw front RGB image. For the LiDAR point clouds, we follow previous works [36, 37, 8] to convert the LiDAR point cloud data into 3-bin histogram over 3-dimensional Bird's Eye View (BEV) grid, resulting in two-channel LiDAR bird's s-ey view projection image input Iiddr.
Output representations Our model generates two types of outputs: safety-insensitiee and safety- sensitive outputs. For safety-insensitiv outputs, InterFuser predicts a path with L = 10 waypoints for the ego vehicle steer toward. It guides the future driving route of the ego vehicle. However, driving along the path without a proper speed might be unsafe and violate actual traffic rules. Therefore, InterFuser additionally predicts the safety-sensitive outputs, consisting of object density map and traffic rule information. The object density map M E ft Rx7 provides 7 features for potential objects in each grid cell, such vehicles, pedestrians and bicycles. Mrj indicates an 1m X Im grid area indexed by spatial coordinates (i, where the ego vehicle is taken as the origin and the y-axis is the forward direction. So the map covers R meters in front of the ego vehicle and It meters 2 on its two sides. The 7 channels in each grid cell are the probability of the existence of object, 2-dimensional offset from the center of the Im x Im grid, the size of the object bounding box, the object heading, and the object velocity. Besides, InterFuser also outputs the traffic rule information, including the traffic light status, whether there is a stop sign ahead, and whether the ego vehicle is at an intersection.
3.2 Model architecture
Backbone For each image input and LiDAR input I E IR we use a conventional CNN backbone ResNet [38] to generate lower-resolution feature map f E RCCHxW We set C = 2048 and (H, W'
He
Wa in experiments.
32
32
Transformer encoder For the feature map f of each sensor input, we first take a 1 1x1 convolution to obtain lower-channel feature map E [R X The spatial dimension of each feature map then collapsed into one dimension, resulting in d x HW tokens. Fixed 2D sinusoidal positional encoding e E Hd is added to each token to retain positional information in each sensor input, and learnable sensor embedding s d added to distinguish tokens from N different sensors:
z (r,9) (a.1)
+ s, + (2.2)
(1)
Where z, represents the tokens extracted from i-th view, x. y denotes the token's 's coordinate index in that sensor. Finally we concatenate the tokens from all sensors, which are then passed through a transformer encoder with K standard transformer layers. Each layer XC consists of Multi-Headed Self-Attentio [7], MLP blocks and layer normalisation [39].
Transformer decoder The decoder follows standard transformer architecture, transforming some query embeddings of size d using K layers of multi-headed self-attention mechanisms. Three types of queries are designed: waypoints queries, R2 density map queries and one traffic rule query. In each decoder layer, we employ these queries to inquire about the spatial information from the multi-modal multi-view features via the attention mechanism. Since the transformer decoder is also permutation-ing invarriann,nnaiinnttt.a the above query embeddings are the same for the decoder and can not produce different results. To this end, we add Icarnable positional embeddings to these query embeddings. The results of these queries are then independently decoded into L waypoints, one density map and the traffic rule by the following prediction headers.
Prediction headers The transformer decoder is followed by three parallel prediction modules to predict the waypoints, the object density map and the traffic rule respectively. For waypoints prediction, following [40, 10), we take single layer GRU [41] to auto-regressively predict sequence of future waypoints [wr)/=1- The GRU predicts the I-th waypoint by taking in the hidden state from step t - 1 and the t-th waypoint embedding from the transformer decoder. Note

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that we first predict each step's differential displacement and then recover the exact position by accumulation. To inform the waypoints predictor the ego vehicle's goal location, we initialize the initial hidden state of GRU with 64-dimensional vector embedded by the GPS coordinates of the goal location with a linear projection layer. For the density map prediction, the corresponding R² d-dimensional embeddingg from the transformer decoder are passed through 3-layer MLP with ReLU activation function to get a R² x feature map. We then reshape it into M E RRx Rxx to obtain the object density map. For traffic rule prediction, the corresponding embedding from the transformer decoder is passed through single linear layer to predict the state of traffic light ahead, whether there is stop sign ahead, and whether the ego vehicle is at intersection.
Loss Function The loss function designed to encourage predicting the desired waypoints (Cpt). object density map (Lmap), and traffic information (C,y):
L = App + Amp + des Xeshef,
(2)
where À balances the three loss terms. Readers can refer to Appendix A for detailed description.
3.3 Safety Controller Leveraging Intermediate Interpretable Features
With the waypoints and intermediate interpretable features (object density map and traffic rule) output from the transformer decoder, we are able to constrain the actions into the safe set. Specifically, we use PID controllers to get two low-level actions: the lateral steering action and the longitudinal acceleration action. The lateral steering action aligns the ego vehicle to the desired heading tha which is simply the average heading of the first two waypoints ahead of ego vehicle. The longitudinal acceleration action aims to catch the desired speed Vd- The determination of uy needs to take surrounding objects into account to ensure safety, for which we resort to the object density map.
The object in grid of the object density map M E RxxRxx is described by an object existence probability, 2-dimensional offset from the grid center, a 2-dimensional bounding box and a heading angle. We recognize the existence of an object in a grid once one of the following criteria is met: 1) if the object's existence probability the grid is higher than threshold,. This criterion intuitive. However, when an object has high position uncertainty, the probability in each grid can be not high enough to reach the threshold, leading to failure in detecting these objects. One workaround is to simply set low threshold,. However, for objects with high uncertainty, the probability in multiple grids can be similar. Thus a low threshold, may lead multiple repeated detections for one exact object. Consequently, following [42, 43], designed the second criteria: 2) if the object existence probability in the grid is the local maximum in surrounding grids and greater than threshold2 (threshold, threshold,). This criterion can detect uncertain objects by a lower threshold,, and avoid repeated detection by only picking the single grid with the highest probability. In addition to the current state of objects, the safety controller also needs to consider their future trajectory. Thus we first design a tracker to monitor and record their historical dynamics, and then predict their future trajectory by propagating their historical dynamics forward in time with moving average.
With the recovered surrounding scene and future forecasting of these objects, we then can obtain 8t, the maximum safe distance the ego-vehicle can drive at time step 1. The desired velocity va with enhanced safety is then derived by solving a linear programming problem. Note that to avoid attractions of unsafe sets and future safety intractability, we also augment the shape of objects, and consider the actuation limit and ego vehicle's ' dynamic constraint. For details of desired velocity va optimization, please refer to Appendix B. In addition to the object density map, the predicted traffic rule is also utilized for safe driving. The vehicle will perform an emergency stop if the traffic light is not green there is stop sign ahead.
Note that while more advanced trajectory prediction methods [44, 45, 46) and safety controller [47, 48] can be used, we empirically found our dynamics propagation with moving average and linear programming controller sufficient. In case of more complicated driving tasks, those advanced algorithms can be casily integrated into our framework in the future.
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Experiments
4.1 Experiment Setup
We implemented our approach on the open-source CARLA simulator with version 0.9. 10.1, including 8 towns and 21 kinds of weather. Please refer to Appendix C for implementation details.
Dataset collection We ran a rule-based expert agent on all kinds of towns and weather with 2 FPS, to collect expert dataset of 3M frames (410 hours) for training and evaluation. Note that the expert agent has the access to the privileged information the CARLA simulator. For the diversity of the dataset, we randomly generated different routes, spawn dynamic objects and adversarial scenarios.
Benchmark We evaluated our methods on three benchmarks: CARLA public leaderboard [49], the Town05 benchmark [8] and CARLA 42 Routes benchmark [17]. In these benchmarks, the ego vehicle required to navigate along predefined routes without collision or violating traffic rules existence of adversarial events?. At each run, the benchmark randomly spawns start/destination points, and generates a sequence of sparse goal locations GPS coordinates. Our method uses these sparse goal locations to guide the driving without manually setting discrete navigational commands (go straight, lane changing, turning). Please refer to Appendix D for detailed descriptions of the three benchmarks.
Three metrics introduced by the CARLA LeaderBoard are used for evaluation: the route completion ratio (RC), infraction score (IS), and the driving score (DS). Route completion is the percentage of the route completed. The infraction score is a performance discount value. When the ego-vehicle commits an infraction or violates a traffic rule, the infractions score will decay by a corresponding percentage. The driving score is the product of route completion ratio and infraction score, and thus a more comprehensive metric describe both driving progress and safety.
Metries
4.2 Comparison to the state of the art
Table compares our method with top 10 state-of-the-art methods on the CARLA leader- board [49]. TCP is an anonymous submission without reference. LAV [51] trains the driving agent with the dataset collected from all the ve- hicles that observes. Transfuser [8, 52] is an imitation learning method where the agent uses transformer fuse information from the front camera image and LiDAR information. The entries "Latent Transfuser" and "Trans- fuser+" are variants of Transfuser. GRIAD [53] proposes to combine benefits from both explo- ration and expert data. Rails [55] uses a tabular mmaami--rrggramnn
Rank
Method
Driving Score
Route Completion
Infraction Score
InterFuser (ours)
76.18
88.23
0.84
TCP [50]
75.14
85.63
LAV [SI]
61.85
94.46
0.64
TransFuser [52]
61.18
86.69
0.71
Latent TransFuser [52]
45.20
66.31
0.72
GRIAD [53]
36.79
61.85
0.60
TransFusert [54]
34.58
69.84
0.56
Rails [55]
31.37
57.65
0.56
TARL. [6]
24.98
46.97
0.52
NEAT [17)
21.83
41.71
0.65
Table I: Performance comparison on the public CARLA leaderboard [49] (accessed 2022). All three met- ries are higher the better. Our Interfuser ranks first on the leaderboard, with the highest driving score, the sec- ond highest route completion, and the second highest. infraction score.
evaluation to compute action-values. IARL [6] is method based on model-free Sinfforeementlearniin
NEAT [17] proposes neural attention fields which enables the reasoning for end-to-end imitation learning.
Our InterFuser ranks first on the leaderboard, with the highest driving score 76.18, the second highest route completion 88.23, and the second highest infraction score 0.84. We also compared our method with other methods on the Town05 benchmark and CARLA 42 Routes benchmark. As shown in Table 4 and Table of the appendix, our method also beats other methods on these two benchmarks.
4.3 Ablation study
On the Town05 Long benchmark, we investigated the influence of different sensor inputs, sen- sor/position embedding, sensor fusion approach, and safety controller. In addition to three metrics, we also present infraction details for comprehensive analysis.
'The adversarial events include bad road conditions, front vehicle's sudden brake, unexpected enti- ties rushing into the road from occluded regions, vehicles running a red traffic light, etc. Please refer to https://eadeebbardd.c wtw.t//wwrdd.....o.oooo orala.org/scenarios for detailed descriptions of adversarial events.

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Driving
Route compl
Infrac.
Collision
Collision
Collision vehicle
Red light
Agent
score.
static
pedestrian
infraction
blocked
%.7 1
%. 1
%
#/Km, L
#/Km,
#/Km,
#KKm. J
#/Km,
F
40.3 + 4.3
99.9
+ 0.0
40.3 5.2
0.09 + 0.01
0.04 + 0.01
0.11 + 0.02
0.09 + 0.02
0 0
F+ Li
17.2 0.9
96.6
11
0.06 2.9
0
0.09 = 0.02
0.10 0.02
0 0
F+LR
43.0 5.4
98.0
2.7
6.2
0.05
0:01
0.01 +
0.09
0.03
0.12+
0 0
F
LR
Fe
46.1 3.1
9..1
48.5
0.01 0.001
0.01
0.1 0.02
0,01 0.02
0.02 + 0.02
LR+
49.2
0.7
89.6
0.7
55.3
2.88
0.01
0:001
0++0
0.09
0.05
0.10
0.02
0.04
0.02
F+ LR Fe+ Li (Ours)
51.6 + 3.4 88.9
+
2.5
58.6
5.2
0.01
0.01
0 0
0.09 0.05 0.02
0.02 0.07 + 0.01
Table 2: Ablation study for different sensor inputs. F, LR, Fe, Li denotes the front view, left and right view, focusing view, and LiDAR BEV representations respectively. t and 4 respectively denote that higher/lower metric value represents better performance. Our method performs the best when all sensor inputs are leveraged.
Driving score
Route compl.
Infrac. score
Collision
Collision
Collision
Red light infraction
Agent blocked
stutic
pedestriun
vehicle
%.
f
%. f
#/Km, L
#/Km, 1
#/Km,
#/Km, 4
#/Km,
No senst/pos embd
48.6
+ 4.7
91.7
+ 2.4
52.6
+ 6.1
0.05 + 0.04
0.01 = 0.01
0.11 4 0.10
0.05
0.01
0.08 0.1
Concutenated input
21.5 + 7.6
97.2
+ 3.9
21.5
8.6
0.06 +
0.03
+ 0.02
0.26 + 0.05
0.20 +: 0.02
0 0.01
attn views
46.8
94.1
3.5
9.
0.07
0.01
0.01
0.01
0.1
0.02
0.01 0.01
No safety contri
43.9 1.7
96.3
4.9
0.05
006:0000 0.03
0.02
= 0.01
0.10
0.10
0.08 0.2
InterFuser (ours)
51.6 3.4
2.5
58.6 5.2
0.01
0.01
0
0.09 + 0.05 0.00
0.07 + 0.01
Table 3: Ablation study for sensor/position embedding (No sensr/pos embd), sensor fusion approach (Con- catenated input, No attn btw views), and safety controller (No safety-contrl). The results demonstrated the effectiveness of these modules.
Sensor inputs As in Table 2, we evaluated the performance when different combinations of sensor inputs are utilized. In the benchmark, adversarial events such as emerging pedestrians or bikes from occluded regions are very common. Since the baseline F only uses the front RGB image input, is hard to notice obstacles a high route completion ratio, it significantly downgraded in the driving score and infraction score.
the sides of the ego vehicle. Therefore, though the baseline F achieved
When the left and right cameras are added, F LR achieved higher driving score and infraction score with effectively reduced collision rate. Since traffic lights are located on the opposite side of the intersection, they usually too far to be detectable the original front image. Adding focusing view for distant sight, LR+ FR + Fc reduced the red light infraction rate by 80% compared to F LR, resulting in higher driving score and infraction score. Equipped with another Lidar input for additional 3D context, our Interfuser F - LR + Fc Li further reduced the collision rate and red light infractions, leading the highest driving score and infraction score.
Sensor Embedding and Positional Encoding In InterFuser, we added the sensor embedding and the positional encoding to help the transformer distinguish tokens in different sensors and different positions in one sensor. As in Table 3, when these operations are removed, the ablation "No sensr/pos embd" had the driving score and the infraction score dropped by 3% and 6% respectively.
Sensor Fusion Methods In Table 3, we also evaluated the performance when different sensor fusion methods are applied. There are two common methods to fuse multi-view or multi-modal inputs: 1) directly concatenating three RGB views along the channel dimension, as in LBC [5] and IA [6]. This ablation "Concatenated input" is assumed to insufficiently fuse information and turned out to have the driving score and infraction score lower by 58% and 63%. 2) stulti-staee fusion of images and LiDAR by transformer in TransFuser [8]. Though the attention mechanism is applied, this method does not consider multi-view fusion and can have problems processing multi-view multi-modal inputs. We implemented this method by designing mask to disallow attention among different views in the transformer encoder, resulting in drops of 9% and 15% on the driving score and the infraction score.
-ehancee controller As in Table 3, when the safety controller is removed and our model directly outputs waypoints, the driving score and the infraction score dropped significantly by 15%. and 22%. Besides, different driving preferences can be generated by assigning different safety factors in the safety controller. As in Fig. 4 of Appendix B, the agents behaves more conservatively with a higher safety factor, leading to higher driving score and infraction score, and lower route completion.
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Left View
Front View
Right View
Object Density Map
Left View
Front View
Right View
(b)
Figure 3: (a) Two cases of how our methnd predicts waypoints and recovers the traffic scene. Blue points denote predicted waypoints. The yellow rectangle represents the ego vehicle, and white/grey rectangles denote the current/future positions of detected objects. (b) Visualization of attention weights the between object density map queries and the features from different views.
4.4 Visualization
In Fig. 3 (a), we visualized two cases to show how our method predicts the waypoints and recovers the surrounding scene. In Fig. 3 (b), also visualize the attention weights the 20 x 20 object density map in the transformer decoder, to show how our method aggregates information from multiple views. As in Fig. (b), our model extracts features from different views for different grids of the object density map queries, For example, the queries on the left of the map mostly attend to the features from the left views. More case visualizations be found in Fig. and Fig. in the Appendix.
Limitation
Though currently ranking the first on public CARLA Leaderboard, method still has traffic infractions. Visualization good cases is provided in Fig. 3 and Fig. 5 of the appendix, and some failure cases provided in Fig. 7 in the appendix. Statistical analysis of the failure causes can found Appendix Currently, we used two-threshold criteria for detection, and dynamic propagation with moving average for trajectory prediction. In the future, more advanced detection and prediction models can be used to further improve the performance. Extending the current deterministic form probabilistic form also help deal with the uncertainty and multi-modalic in detection and prediction. Rigorous theoretical proof on the safety and interpretability is also an important future work. Besides, all this work happened in simulation, where driving scenarios limited. On actual roads where countless driving situations exist, enhancing the generalizability of our method would be vital for scalable deployment.
6 Conclusion
We present InterFuser, novel autonomous driving framework based an interpretable sensor fusion transformer. The driving agent obtains a perception of global context from multi-view multi-modal inputs and generates interpretable intermediate outputs. The interpretable outputs are utilized by a safety-enhanced controller to constrain actions within the safe set. InterFuser sets new performance upper-bound in complex and adversarial urban scenarios future work includes importing temporal information into the pipeline for more accurate and stable prediction of other objects' future trajectories. Given that our method is flexible and extendable, it would also interesting to explore different types of interpretable intermediate outputs, or use more advanced controllers.
the CARLA. Our
Acknowledgment
The authors would like to thank Chuming Li for insightful discussions. The work was supported by the National Key R&D Program of China under Grant 20212D0201300.

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12


Reviews:
[{"review_id": "p9il4rLkHFQ", "reviewer": "Reviewer_snM2", "summary": "This paper proposed a transformer fusion architecture for controlling autonomous driving agents. Various image and LiDAR inputs are processed by CNNs and fused by a transformer encoder, which is followed by a transformer decoder to output driving action and auxiliary outputs. Evaluation results on various benchmarks, including the public CARLA leaderboard, demonstrate the effectiveness of the proposal. ", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths: \n\n1. This paper covers an important topic. \n\n2. The proposal achieves impressive performance. \n\n3. The writing is generally clear and the diagrams are helpful for understanding the architecture. \n\nWeaknesses: \n\nMy main concern about this paper is the lack of more careful analysis of safety and interpretability, the two main benefits claimed by this paper. These two concepts are very related in this paper, as safety is ensured by the \"interpretable\" intermediate outputs generated by the model, such as inferred traffic state information. \n\nHowever, since these outputs are generated by equally non-interpretable black box model (actually the same transformer decoder), so this set up seems more like an auxilliary loss setup, rather than providing any kind of interpretability or safety guarantee. \n\nFor safety, it is mainly tackled by verification, as already discussed in the related work, or barrier functions [e.g. 1]. However, the safety notion strongly depends on the quality of the intermediate output prediction, which seems hard to offer any guarantee. \n\nOn the interpretability side, I wouldn't call such an architecture interpretable. Specifically, it is not clear what additional benefits the \"explanation\" (i.e. intermediate outputs) offer, in terms of understanding the model. Because the explanation is generated along side the action predictions, they do not need to be coupled in anyway, so that, for example, the action could be the \"drive forward\" prediction even if a red traffic light is also predicted. For claims on interpretability, I would like to see some concrete evidence, such as helping with model debugging [2, 3], improving human-model collaboration [4], or some other use cases [5]. \n\nAt the very least, some more careful analysis of the intermediate output is needed to understand when they can help and when they cannot. Then the lack of rigorous studies of safety and interpretability could be acknowledged in the limitation section. \n\n[1] https://arxiv.org/abs/2109.06689\n\n[2] https://arxiv.org/abs/2104.14403\n\n[3] https://openreview.net/forum?id=xNOVfCCvDpM\n\n[4] https://arxiv.org/abs/2006.14779\n\n[5] https://dl.acm.org/doi/10.1145/3511299", "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": "", "summary_of_recommendation": "I believe that this paper could make an important contribution, but at this moment, neither of the central claims, safety and interpretability, are supported by strong empirical evidence. However, if such evidence is convincingly demonstrated, this paper could have potentially high impact. ", "technical_quality": "Fair: Significant additional evidence required to support the claims.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Highly relevant to robotics but no hardware experiments", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "issues": "See concerns in the weakness section. ", "reviewer_expertise": "3: The reviewer is fairly confident that the evaluation is correct"}, "extra_text": {}}, {"review_id": "3yjSUFKEEcU", "reviewer": "Reviewer_djxH", "summary": "The authors present an interpretable autonomous vehicle (AV) policy which features a sensor fusion transformer. The authors develop a transformer encoder which takes in which uses multiple camera viewpoints and LIDAR. Additionally, a transformer decoder which output waypoints, an object density map, and traffic rules in order to determine a nominal trajectory and use a safety controller to adjust the velocity of the planned trajectory. The resulting AV policy beats state of the art methods in CARLA leaderboard and benchmarks.\n", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- Justification for methods are well argued and the paper is accessible \n- The results on the CARLA leaderboard and benchmarks are impressive and show the potential of the method for more realistic/larger scale settings. \n- The inclusion of code for the method is a great strength and I hope it is made public upon publication. This will allow follow up work to more easily compare to this strong method and in a straightforward way and extend portions of the method (such as with different safety controllers as the authors mention on L210-L11).\n\nWeaknesses:\n- The addition of an ablation study is important in understanding the importance of the choices the authors made in the resulting method. However, as it stands, the interpretation of the ablation study and tables 2 and 3 are quite confusing and I am not sure how to view the results. The authors seem to have a different interpretation of the table than I do, but this is possibly due to a mistake in the table.\n- The overall approach is very hand-crafted and there are many possibly non-obvious choices which must be made. E.g. L554-L555 cyclists’ and pedestrians’ bounding boxes are scaled up but not vehicles’. I believe this is common for large-scale learning for AVs but this does somewhat weaken the approach. It is possible that the good performance is due to these many choices and the careful cost function shaping instead of the transformer and interpretable outputs. ", "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": "", "summary_of_recommendation": "The method uses transformers in an AV sensor fusion problem and leverages them to provide useful, interpretable, intermediate features which are then used improve the safety of a planned trajectory. The results are impressive and show the strength and potential of the methods. The method seems easily adjustable to suit other problems and are amenable to follow up work. Therefore, I recommend acceptance.\n\n--- \n\n**Update after revisions and replies**: The authors have address all major issues and weaknesses. I continue to support acceptance.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Excellent: Enjoyable to read.", "potential_impact": "", "robotics_focus": "Highly relevant to robotics but no hardware experiments", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "4: The reviewer is confident but not absolutely certain that the evaluation is correct"}, "extra_text": {"issues": "- Related work: The authors should include and distinguish their work from “End-To-End Interpretable Neural Motion Planner”, Zeng et al. CVPR 2019. The interpretable portion of this method is similar in concept to the one presented. \n\n- L218: The authors do not seem to elaborate on the rule-based expert used to generate the experimental data. Is the expert based on another paper? If not, what are the rules? How important is the performance of the expert to the downstream performance of InterFuser? Would an human driver expert be preferable because of the larger variety of behaviors?\n\n- What is the inference time for the InterFuser? Can the method be used for real-time planning/control in AV settings? If not, I suggest including this in the limitations. \n\n- Table 2 and 3: I believe the authors have switched the route completion and infraction score column labels. If not, the interpretation of the tables 2 and 3 in L263-L288 seems incorrect. Additionally, if not, the authors need to explain why the infraction score gets worse when adding additional sensor inputs or when removing effective fusing techniques, which doesn’t make sense to me. \n\n- Table 2 and 3: It would be helpful to include other methods to compare to in Tables 2 and 3. If other methods outperform e.g. “concatenated input” in Table 3, then it’s clear the sensor fusion portion of InterFuser is vital to success. On the other hand, if they do not outperform concatenated input, then it may be that other choices in designing the system results in the better performance.\n\n- Appendix C: The authors should consider including a section with training time, resources used for the results in the paper, and number of parameters in the InterFuser. Since AV networks can be extremely expensive to train and evaluate, it is important to consider the accessibility of such methods which require expensive computing equipment to train."}}, {"review_id": "2FBSWOaSMt", "reviewer": "Reviewer_8oVA", "summary": "This paper introduces a sensor-fusion approach that provides interpretable intermediate representations of the world scene. The approach can fuse multi-view RGB images along with LiDAR scans. The network architecture involves a CNN backbone which feeds into a transformer encoder for fusion. A transformer decoder generates an object density map, a set of waypoints for the ego, and a set of rules to be enforced (such as traffic lights) which are then fused in a safety controller to generate open-loop actions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n1. I like the idea of training the perception pipeline with the planning and control modules in tow. This would allow the perception pipeline to extract the features that are relevant to behavior generation and control.\n2. The approach provides interpretable perception outputs which would be a great asset for verification of the correctness of the decisions made by the downstream planning and control modules, modulo the correctness of the perception outputs.\n3. The experimental results are strong (first rank in CARLA leaderboard in driving score) and thorough (extensive ablation studies that do give reasonable insights such as the importance of fusion).\n\nWeaknesses:\n1. In a simulator, the data collection for training was easy because direct access to the scene ground truth is available. However, perfectly annotating the object density map for training from real driving logs will be very challenging.\n2. The planning and control modules in an actual AV might be significantly more complex than the safety controller used in this paper with multiple layers and potentially non-differentiable components.\n3. The paper suffers from some grammatical errors which can be fixed.\n\nClarification question:\n1. In the context of control, line 109 says: “rule-based methods hardly scale to complex environments due to the extensive human labor required.” What human labor is being discussed here? Also, could you cite a reference which suggests rule-based methods fail to scale. This paper (Helou, Bassam, et al. \"The Reasonable Crowd: Towards evidence-based and interpretable models of driving behavior.\" IROS 2021) seems to suggest otherwise.\n2. Why does picking the local maximum for object probability in the map help with identifying objects with high position uncertainty (as suggested in line 198)?\n", "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": "", "summary_of_recommendation": "I recommend a Weak Accept. The paper is good and provides detailed experimental results. My reservations listed in weaknesses are of an “open-problems” nature.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "Highly relevant to robotics but no hardware experiments", "quality_of_limitations": "Limitations are addressed clearly", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "3: The reviewer is fairly confident that the evaluation is correct"}, "extra_text": {"issues": "1. Please clarify your comment about the scalability of rule-based control methods.\n2. Please clarify why local max probability helps with objects suffering from high position uncertainty.\n3. Please fix the grammar issues in the paper.\n"}}]

Author rebuttal:

Decision: Accept (poster)