When it comes to autonomous driving, Transformer has consistently been a pivotal technology. Why is Transformer a recurring topic in the autonomous driving sector? The answer lies in its architecture, which inherently boasts numerous advantageous features for processing multi-source, high-dimensional, and long-sequence data. It excels at modeling long-range dependencies, facilitating multimodal fusion, enabling parallel training, supporting large-scale pre-training and transfer learning, and employing a relatively unified framework for perception, tracking, prediction, and even certain decision-making tasks. Today, let's delve deeper into the workings of Transformer.

What Exactly Is Transformer?

Before delving into today's topic, it's crucial to grasp what Transformer is. Imagine sitting in a café, observing the traffic at an intersection outside. You notice a car turning, a pedestrian halting, and a traffic light transitioning from green to yellow. To predict who will move first in the next second, you can't rely solely on the most recent frame; instead, you must synthesize the actions from the past few seconds, the relative positions of different traffic entities, the traffic light status, and the road's geometry. The core idea behind Transformer is to equip the model with the ability to 'directly communicate between any two input elements.' Unlike traditional models that sequentially pass information over time, Transformer achieves this 'direct communication' through a mechanism known as self-attention. Self-attention calculates, for each element in the input sequence, which other elements it should pay more attention to, and then integrates these crucial pieces of information to form a meaningful representation for the current element. To put it simply, self-attention resembles a discussion forum where anyone can immediately hear and respond to anyone else's comments, adjusting their views accordingly, rather than passing messages sequentially through rows of people.

In autonomous driving, Transformer intuitively manifests by mapping each input (such as a pixel block in an image frame, a segment of radar echo, or a feature from a timestamp) into three types of vectors: query, key, and value. The query asks, 'What do I want to know?' the key represents, 'What clues do I have here?' and the value is 'What I actually want to transmit.' The essence of self-attention lies in matching the query with all keys for similarity, using the resulting weights to weightedly sum the corresponding values, and then representing the fused information. In this manner, similar or related information reinforces each other, while the weights of irrelevant information are diminished. To address the issue of the lack of explicit order in the input (e.g., word order is crucial in text, but self-attention itself is orderless), Transformer introduces positional encoding, injecting positional information into each element's representation to retain cues about temporal or spatial order.

The original Transformer architecture comprises two parts: an encoder and a decoder. The encoder encodes the input into a set of high-dimensional representations, while the decoder progressively generates outputs in conditional generation tasks (e.g., generating target sentences word by word in machine translation). However, in visual or perception tasks, many approaches simplify it by using only the encoder for feature extraction or extending the encoder's idea into different variants adapted to inputs like images, point clouds, and videos. Compared to RNNs (Recurrent Neural Networks), a significant engineering advantage of Transformer is parallelization. RNNs process data recursively step by step in time, making full parallelization during training impossible. In contrast, Transformer's self-attention can be computed in parallel along the temporal or spatial dimensions, giving it a substantial edge in training speed on large-scale datasets.

Advantages of Transformer in Autonomous Driving

At the perception level, autonomous driving aims to answer, 'What is here, where is it, and how might it move?' Traditional visual detection or radar processing typically relies on convolutional neural networks (CNNs) for local feature extraction, combined with specialized post-processing and heuristic trackers. One of Transformer's significant advantages is its global receptive field, allowing direct connections between any two positions at the same level. This is particularly beneficial for identifying occluded objects and handling long-range associations (e.g., subtle motions of distant vehicles indicating lane changes). For instance, when branches partially occlude a distant pedestrian in the camera's view, a convolutional architecture might require numerous layers to propagate the complete semantic information from afar. In contrast, self-attention can directly 'recall' the complete features of the distant pedestrian to supplement the local missing information, thereby enhancing detection robustness.

In multi-sensor fusion, autonomous driving systems typically need to integrate information from cameras, LiDAR, millimeter-wave radar, and inertial navigation. Traditional methods often perform independent feature extraction for each sensor and then fuse them using rules or shallow networks. Transformer offers a more natural fusion approach by treating features from various sensors as a set of 'tokens' and letting the self-attention mechanism learn the relationships between different modalities. It can automatically decide when to prioritize visual information and when to rely on the radar's distance accuracy, without manually setting which modality has higher weight. This is especially crucial in complex weather or lighting conditions, such as foggy days when camera information degrades, but radar and LiDAR still provide reliable cues. Transformer can learn during training how to dynamically adjust attention allocation under these conditions.

Time series and prediction are another core task in autonomous driving. Autonomous vehicles must not only perceive the current world but also predict the trajectories of surrounding traffic entities within the next few seconds to make informed decisions. RNNs can handle time series, but their ability to model long-term dependencies is limited, and training is not easily parallelizable. Traditional sliding window feature + convolutional approaches also overlook the influence of distant moments on current decisions. Transformer's self-attention is naturally adept at modeling long-range dependencies, enabling it to consider data from several seconds or even dozens of frames together, allowing the model to select the most useful information from the entire history for current predictions. For example, if a vehicle has been making subtle deviations over the past few seconds, this trend information might be crucial for predicting its future lane change. Transformer can directly combine these early subtle signals with recent frames to produce more reliable predictions.

End-to-end and simplified pipelines are another reason for Transformer's popularity. Traditional autonomous driving perception often follows a 'divide and conquer' approach, with separate modules and complex intermediate representations for detection, tracking, segmentation, prediction, and planning. Transformer offers the possibility of unifying multiple tasks into a single network or a general-purpose backbone. Self-attention can simultaneously output detection boxes, tracking IDs, semantic segmentation, and prediction vectors on the same representation, providing significant advantages in reducing engineering interfaces, minimizing error accumulation, and facilitating end-to-end optimization. Of course, this does not imply that all scenarios can completely abandon modularity, but a unified architecture does offer cleaner optimization goals and fewer manual rules.

Another advantage of Transformer is its scalability and pre-training ecosystem. In the NLP field, Transformer has demonstrated that large models, combined with large datasets and a pre-training-finetuning approach, can transform general representations into highly useful starting points for downstream tasks. Applying a similar strategy to vision and multimodal tasks, the autonomous driving field can leverage large-scale simulated data, unlabeled videos, synthetic point clouds, etc., for self-supervised pre-training. Then, fine-tuning the pre-trained network on annotated data can significantly improve sample efficiency and robustness. For practical manufacturers, this means transforming vast amounts of 'unlabeled' or 'weakly labeled' data into valuable information, reducing reliance on expensive manual annotation.

Transformer's parallelization characteristics result in better training speed and hardware utilization on modern accelerators (GPUs/TPUs). RNNs' sequential processing design limits efficiency during large-scale data training, while Transformer's parallel computability along temporal or spatial dimensions naturally shortens training cycles, especially during large-scale pre-training. Moreover, Transformer's modularity (attention layers + feedforward layers) also facilitates model parallelization and pipeline segmentation, making it easier to scale up to models with hundreds of millions or billions of parameters.

Beyond these 'capability-level' advantages, Transformer also offers opportunities in model interpretability. Although attention is not a perfect explanatory tool, attention weights are often used to observe the regions the model focuses on, which is helpful in debugging perception failures or understanding why the model makes mistakes in specific scenarios. For instance, if the model misjudges a stationary object as a pedestrian, examining the attention can reveal that the model focused more on a background region or a reflective spot, providing clues for subsequent corrections.

When assisting autonomous vehicles in perceiving the environment, Transformer's most significant engineering value lies in tasks requiring global information, cross-modal associations, or long-term dependencies. For example, in multi-object tracking and joint detection-tracking, placing detection and tracking under the same attention mechanism can significantly reduce erroneous linkages. In trajectory prediction, modeling historical trajectories, map semantics, and neighboring vehicle interactions as tokens together can more naturally capture interaction patterns. In BEV (Bird's Eye View) perception, Transformer helps unify modeling when projecting multiple cameras and sparse LiDAR data into the same BEV space, resulting in a consistent scene understanding. In short, when a problem requires aggregating dispersed information into a unified view and reasoning about interrelationships, Transformer is usually a powerful choice.

What Are the Shortcomings of Transformer?

We've been discussing Transformer's advantages, but does it have any shortcomings? The standard self-attention computation complexity grows quadratically with the number of tokens, quickly becoming a bottleneck for high-resolution images or fine-grained point clouds. Currently, common solutions fall into two categories: first, reducing the number of tokens, such as downsampling images, using convolutions to extract local features before global attention, or employing sparse/local attention mechanisms that only compute within adjacent regions; second, adopting hierarchical structures that limit attention to local areas and then propagate global information across layers (similar to hierarchical variants of vision Transformers). These compromises can maintain Transformer's advantages while controlling computational load, but they increase design and tuning costs.

Transformer also requires substantial data and computing power to achieve maximum benefits. Annotating data for autonomous driving is costly, and real-world driving scenarios suffer from severe long-tail problems. Relying solely on supervised learning often leads to overfitting mainstream scenarios. Therefore, in practice, self-supervised learning, synthetic data, and simulator data from reinforcement learning are combined to alleviate data scarcity. The pre-training-finetuning strategy is particularly crucial here, but how to connect general pre-training with lightweight models that run in real-time on vehicles remains a challenge.

Latency and energy consumption during deployment are also very real issues. Vehicle systems have stringent requirements for real-time performance and power consumption, especially in low-cost mass-produced vehicles, where deploying a Transformer with hundreds of millions of parameters is not feasible. Common practices include placing large models in the cloud or edge servers for perception/prediction and then compressing the results for transmission back to the vehicle, or distilling the model into a lightweight version for onboard deployment. Each choice involves trade-offs; cloud-based solutions suffer from communication latency and coverage limitations, while onboard quantization/distillation may sacrifice some accuracy.

Although attention provides some 'visualizable' clues, it does not equate to strict interpretability or safety guarantees. In safety-critical scenarios like autonomous driving, relying solely on the intuitive explanations from attention is insufficient to meet verification and certification requirements. Engineering-wise, additional verification, robustness testing, formal methods, or redundant systems are necessary to ensure safety.

The autonomous driving industry has made numerous adaptations when introducing Transformer into engineering. For example, there are various variants in how to tokenize image/point cloud/radar data; some approaches first use CNNs to extract local features and then input patch-level tokens into Transformer, while others directly slice point clouds into small token blocks. For time series, tokens from different timestamps are often concatenated for temporal attention, or temporal attention is stacked on top of spatial attention. To control complexity, strategies like sparse attention, grouped attention, and sliding window attention are also employed. All these highlight the fact that Transformer is a highly flexible 'toolbox,' but whether it works well and how to use it effectively still require engineering design and extensive experimental tuning.

How to Actually Apply Transformer in Autonomous Driving?

When applying the Transformer model to autonomous driving systems, we need to clarify a few key considerations. Firstly, it's unrealistic to view Transformer as a "one-size-fits-all" solution that can directly substitute for all existing modules. In practice, a judicious integration of Transformer with convolutional layers, graph neural networks, and physical priors typically leads to superior performance. Secondly, it's crucial to manage computational resources and latency effectively. During the training phase, utilizing large-scale models is feasible, but for deployment, strategies such as model distillation, quantization, pruning, or hierarchical model deployment should be planned in advance. Thirdly, it's essential to maximize the use of self-supervised learning and simulated data. Pre-training proves highly advantageous when labeled samples are limited, particularly if one can gather substantial volumes of unlabeled driving videos and sensor data streams. Fourthly, robustness testing should be prioritized. Instead of solely relying on average performance metrics on clean datasets, it's imperative to conduct robustness evaluations under challenging conditions, such as adverse weather, extreme lighting, or sensor malfunctions. Fifthly, to meet stringent safety standards, interpretability tools should be combined with redundant system designs. While attention mechanisms can provide a useful starting point for debugging, more rigorous verification processes are indispensable to guarantee functional safety.

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