Autonomous driving systems need to face various complex scenarios in practical applications, especially Corner Cases (extreme situations) which place higher requirements on the perception and decision-making capabilities of autonomous driving. Corner Case refers to extreme or rare situations that may occur in actual driving, such as traffic accidents, severe weather conditions or complex road conditions. BEV technology enhances the perception capabilities of autonomous driving systems by providing a global perspective, which is expected to provide better support in handling these extreme situations. This article will explore how BEV (Bird's Eye View) technology can help the autonomous driving system cope with the Corner Case and improve the reliability and safety of the system.
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Transformer is a deep learning model based on the self-attention mechanism and was first used in natural language processing tasks. . The core idea is to capture long-distance dependencies in the input sequence through a self-attention mechanism, thereby improving the model's ability to process sequence data.
The effective combination of the above two is also a very popular emerging technology in autonomous driving strategies.
BEV is a method of projecting three-dimensional environmental information onto a two-dimensional plane, displaying it from a top-down perspective Objects and terrain in the environment. In the field of autonomous driving, BEV can help the system better understand the surrounding environment and improve the accuracy of perception and decision-making. In the environment perception stage, BEV can fuse multi-modal data such as lidar, radar and camera on the same plane. This method can eliminate occlusion and overlap problems between data and improve the accuracy of object detection and tracking. At the same time, BEV can provide a clear representation of the environment for subsequent prediction and decision-making stages, which is beneficial to improving the overall performance of the system.
First of all, BEV technology can provide a global perspective of environmental perception, helping to improve automatic The performance of the driving system in complex scenarios. However, lidar has higher accuracy in terms of distance and spatial information.
Secondly, BEV technology captures images through cameras and can obtain color and texture information, while lidar's performance in this regard is weak.
In addition, the cost of BEV technology is relatively low and suitable for large-scale commercial deployment.
The traditional single-view camera is a commonly used vehicle sensing device that can capture Environmental information around the vehicle. However, single-view cameras have certain limitations in terms of field of view and information acquisition. BEV technology integrates images from multiple cameras to provide a global perspective and a more comprehensive understanding of the environment around the vehicle.
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BEV technology has better environmental perception compared to single-view cameras in complex scenes and severe weather conditions Ability, because BEV can fuse image information from different angles, thereby improving the system's perception of the environment.
BEV technology can help autonomous driving systems better handle corner cases, such as complex road conditions, narrow or blocked roads, etc., while single-view cameras may not perform well in these situations .
Of course, in terms of cost and resource usage, BEV requires image perception, reconstruction and splicing from various viewing angles, so it consumes more computing power and storage resources. Although BEV technology requires the deployment of multiple cameras, the overall cost is still lower than lidar, and its performance is significantly improved compared to single-view cameras.
To sum up, BEV technology has certain advantages compared with other perception technologies in the field of autonomous driving. Especially when it comes to processing Corner Cases, BEV technology can provide a global perspective of environmental perception, helping to improve the performance of autonomous driving systems in complex scenarios. However, in order to fully leverage the advantages of BEV technology, further research and development are still needed to improve performance in image processing capabilities, sensor fusion technology, and abnormal behavior prediction. At the same time, combined with other perception technologies (such as lidar) and deep learning and machine learning algorithms, the stability and safety of the autonomous driving system in various scenarios can be further improved.
At the same time, Bird's Eye View (BEV) serves as an effective environment perception Methods play an important role in autonomous driving systems. Combining the advantages of Transformer and BEV, we can build an end-to-end autonomous driving system to achieve high-precision perception, prediction and decision-making. This article will also explore how Transformer and BEV can be effectively combined and applied in the field of autonomous driving to improve system performance.
The specific steps are as follows:
Combine lidar, Multi-modal data such as radar and camera are fused into BEV format, and necessary preprocessing operations are performed, such as data enhancement, normalization, etc.
First, we need to convert multi-modal data such as lidar, radar and camera to BEV format. For lidar point cloud data, we can project the three-dimensional point cloud onto a two-dimensional plane, and then rasterize the plane to generate a height map; for radar data, we can convert the distance and angle information into a height map. Karl coordinates are then rasterized on the BEV plane; for camera data, we can project the image data onto the BEV plane to generate a color or intensity map.
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Perception in autonomous driving stage, the Transformer model can be used to extract features in multi-modal data, such as lidar point clouds, images, radar data, etc. By conducting end-to-end training on these data, Transformer can automatically learn the intrinsic structure and interrelationships of these data, thereby effectively identifying and locating obstacles in the environment.
Use the Transformer model to extract features from BEV data to detect and locate obstacles.
Superimpose these BEV format data together to form a multi-channel BEV image. Assume that the BEV height map of the lidar is H(x, y), the BEV range map of the radar is R(x, y), and the BEV intensity map of the camera is I(x, y), then the multi-channel BEV image can be expressed as ：
##B(x, y) = [H(x, y), R(x, y), I(x, y)]
Where B(x, y) represents the pixel value of the multi-channel BEV image at coordinates (x, y), [] represents channel superposition.
Based on the output of the perception module, use the Transformer model to predict the future behavior and trajectory of other traffic participants. By learning historical trajectory data, Transformer is able to capture the movement patterns and interactions of traffic participants, thereby providing more accurate predictions for autonomous driving systems.
Specifically, we first use Transformer to extract features from multi-channel BEV images. Assuming that the input BEV image is B(x, y), we can extract features F(x, y) through multi-layer self-attention mechanism and position encoding:
F(x, y) = Transformer(B(x, y))
where F(x, y) represents the feature map, the feature value at coordinates (x, y).
Then, we use the extracted features F(x, y) to predict the behaviors and trajectories of other traffic participants. Transformer's decoder can be used to generate prediction results, as follows:
P(t) = Decoder(F(x, y), t)
Where P(t) represents the prediction result at time t, and Decoder represents the Transformer decoder.
Through the above steps, we can achieve data fusion and prediction based on Transformer and BEV. The specific Transformer structure and parameter settings can be adjusted according to actual application scenarios to achieve optimal performance.
Based on the results of the prediction module, combined with traffic rules and vehicle dynamics models, the Transformer model is used to generate appropriate Driving strategy.
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By integrating environmental information, traffic rules and vehicle dynamics models into the model, Transformer can learn efficient and safe driving strategies. Such as path planning, speed planning, etc. In addition, using Transformer's multi-head self-attention mechanism, the weights between different information sources can be effectively balanced to make more reasonable decisions in complex environments.
The following are the specific steps to adopt this method:
First of all, a large amount of driving data needs to be collected, including vehicle status information (such as speed, acceleration, steering wheel angle, etc.), road condition information (such as road type, traffic signs, lane lines, etc.), surrounding environment information (such as other vehicles, pedestrians, cyclists, etc.) and the actions taken by the driver. These data are preprocessed, including data cleaning, standardization and feature extraction.
Encode the collected data into a form suitable for Transformer model input. This typically involves discretizing continuous numerical data and converting the discretized data into vector form. At the same time, the data needs to be serialized so that the Transformer model can handle timing information.
2.1. Transformer encoder
Transformer encoder consists of multiple identical sub-layers. Each sub-layer Contains two parts: Multi-Head Attention and Feed-Forward Neural Network.
Multi-head self-attention: First divide the input sequence into h different heads, calculate the self-attention of each head separately, and then splice the output of these heads together. This captures dependencies at different scales in the input sequence.
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The calculation formula of bull’s self-attention is:
MHA (X) = Concat(head_1, head_2, ..., head_h) * W_O
where MHA(X) represents the output of multi-head self-attention, and head_i represents the output of the i-th head , W_O is the output weight matrix.
Feedforward Neural Network: Next, the output of the multi-head self-attention is passed to the feedforward neural network. Feedforward neural networks usually contain two fully connected layers and an activation function (such as ReLU). The calculation formula of the feedforward neural network is:
FFN(x) = max(0, xW_1 b_1) * W_2 b_2
where FFN (x) represents the output of the feedforward neural network, W_1 and W_2 are weight matrices, b_1 and b_2 are bias vectors, and max(0, x) represents the ReLU activation function.
In addition, each sub-layer in the encoder contains residual connections and layer normalization (Layer Normalization), which helps to improve the training stability and convergence speed of the model.
2.2. Transformer decoder
Similar to the encoder, the Transformer decoder also consists of multiple layers of the same sub-layers. Each sub-layer consists of three parts: multi-head self-attention, encoder-decoder attention (Encoder-Decoder Attention) and feed-forward neural network.
Multi-head self-attention: The same as the multi-head self-attention in the encoder, used to calculate the degree of correlation between each element in the decoder input sequence.
Encoder-decoder attention: used to calculate the degree of correlation between the decoder input sequence and the encoder output sequence. The calculation method is similar to self-attention, except that the query vector comes from the decoder input sequence, and the key vector and value vector come from the encoder output sequence.
Feedforward neural network: Same as the feedforward neural network in the encoder. Each sub-layer in the decoder also contains residual connections and layer normalization. By stacking multiple layers of encoders and decoders, Transformer is able to handle sequence data with complex dependencies.
Build a Transformer model suitable for autonomous driving scenarios, including setting the appropriate number of layers and heads. and hidden layer size. In addition, the model also needs to be fine-tuned according to task requirements, such as using a driving policy to generate a loss function for the task.
First, the feature vector is obtained by MLP to obtain a low-dimensional vector, which is passed to the automatic regression path point network implemented by GRU, and used to initialize the hidden state of GRU. In addition, the current position and target position are also input, causing the network to focus on the relevant context of the hidden state.
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Use a single layer GRU and use a linear layer to predict the path point offset from the hidden state
, get the predicted path point 
. The input to the GRU is the origin.
The controller uses two PID controllers to perform horizontal and longitudinal control respectively based on the predicted path points to obtain steering, braking and throttle values. Perform a weighted average of the path point vectors of consecutive frames, then the input of the longitudinal controller is its module length, and the input of the transverse controller is its orientation.
Calculate the L1 loss of the expert trajectory path points and predicted trajectory path points in the current frame's self-vehicle coordinate system, that is,
Use the collected data set to train the Transformer model. During the training process, the model needs to be validated to check its generalization ability. The data set can be divided into training, validation, and test sets to evaluate the model.
In actual applications, pre-trained data is input based on the current vehicle status, road condition information and surrounding environment information. Transformer model. The model will generate driving strategies such as acceleration, deceleration, steering, etc. based on these inputs.
Pass the generated driving strategy to the automatic driving system to control the vehicle. At the same time, data from the actual execution process are collected for further optimization and iteration of the model.
Through the above steps, a method based on the Transformer model can be used to generate an appropriate driving strategy in the autonomous driving decision-making stage. It should be noted that due to the high safety requirements in the autonomous driving field, it is necessary to ensure the performance and safety of the model in various scenarios during actual deployment.
In this section, we will introduce in detail three examples of BEV technology solving Corner Case Examples include complex road conditions, severe weather conditions, and predicting abnormal behavior. The following figure shows some cornercase scenarios in autonomous driving. The technology of Transformer BEV can effectively identify and deal with most of the edge scenes that can currently be identified.
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In complex road conditions Under conditions such as traffic jams, complex intersections or irregular road surfaces, Transformer BEV technology can provide more comprehensive environmental perception. By integrating images from multiple cameras around the vehicle, BEVs generate a continuous overhead view, allowing the autonomous driving system to clearly identify lane lines, obstacles, pedestrians and other traffic participants. For example, at a complex intersection, BEV technology can help the autonomous driving system accurately identify the location and driving direction of each traffic participant, thereby providing a reliable basis for path planning and decision-making.
In bad weather conditions, such as rain, snow, fog, etc., traditional cameras and lidar It may be affected and reduce the perception ability of the automatic driving system. Transformer BEV technology still has certain advantages in these situations because it can fuse image information from different angles, thereby improving the system's perception of the environment. In order to further enhance the performance of Transformer BEV technology in severe weather conditions, you can consider using auxiliary equipment such as infrared cameras or thermal imaging cameras to supplement the deficiencies of visible light cameras in these situations.
In the actual road environment, pedestrians, cyclists and other traffic participants may have abnormal behaviors, such as suddenly crossing the road, violating Traffic rules etc. BEV technology can help autonomous driving systems better predict these abnormal behaviors. With the global perspective, BEV can provide complete environmental information, allowing the autonomous driving system to more accurately track and predict the dynamics of pedestrians and other traffic participants. In addition, by combining machine learning and deep learning algorithms, Transformer BEV technology can further improve the prediction accuracy of abnormal behaviors, allowing the autonomous driving system to make more reasonable decisions in complex scenarios.
In narrow or blocked road environments, traditional cameras and lidar may have difficulty obtaining adequate information for effective environmental perception. However, Transformer BEV technology can come into play in these situations because it can integrate images captured by multiple cameras to generate a more comprehensive view. This allows the autonomous driving system to better understand the environment around the vehicle, identify obstacles in narrow passages, and safely navigate these scenarios.
In scenarios such as highways, autonomous driving systems need to deal with complexities such as merging vehicles and traffic merging. Task. These tasks place high demands on the perception capabilities of the autonomous driving system, as the system needs to evaluate the position and speed of surrounding vehicles in real time to ensure safe merging and traffic merging. With Transformer BEV technology, the autonomous driving system can gain a global perspective and clearly understand the traffic conditions around the vehicle. This will help the autonomous driving system develop appropriate merging strategies to ensure that vehicles can safely integrate into the traffic flow.
In emergency situations, such as traffic accidents, road closures or emergencies, the autonomous driving system needs to be fast Make decisions to ensure safe driving. In these cases, Transformer BEV technology can provide real-time, comprehensive environmental awareness for the autonomous driving system, helping the system quickly assess the current road conditions. Combining real-time data and advanced path planning algorithms, autonomous driving systems can develop appropriate emergency strategies to avoid potential risks.
Through these examples, we can see that Transformer BEV technology has great potential in dealing with Corner Case. However, in order to give full play to the advantages of Transformer BEV technology, further research and development are still needed to improve performance in image processing capabilities, sensor fusion technology, and abnormal behavior prediction.
This article summarizes the principles and applications of Transformer and BEV technology in autonomous driving, especially how to solve the Corner Case problem. By providing a global perspective and accurate environmental perception, Transformer BEV technology is expected to improve the reliability and safety of autonomous driving systems in the face of extreme situations. However, current technology still has certain limitations, such as performance degradation in adverse weather conditions. Future research should continue to focus on the improvement of BEV technology and its integration with other sensing technologies to achieve a higher level of autonomous driving safety.
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