B OOTSTRAP M OTION F ORECASTING W ITH S ELFC ONSISTENT C ONSTRAINTS
Anonymous authors
Paper under double-blind review
A BSTRACT
We present a novel framework to bootstrap M otion forecast
I ng with S elfconsistent C onstraints (MISC).
The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past.
A key design of MISC is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during training.
Also, to model the multi-modality in motion forecasting, we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision.
With explicit constraints from multiple teacher targets, we observe a clear improvement in the prediction performance.
Extensive experiments on the Argoverse motion forecasting benchmark show that MISC significantly outperforms the state-of-theart methods.
As the proposed strategies are general and can be easily incorporated into other motion forecasting approaches, we also demonstrate that our proposed scheme consistently improves the prediction performance of several existing methods.
I NTRODUCTION
Motion forecasting has been a crucial task for self-driving vehicles that aims at predicting the future trajectories of agents (e.g., cars, pedestrians) involved in the traffic.
The predicted trajectories can further help self-driving vehicles to plan their future actions and avoid potential accidents.
Since the future is not deterministic, motion forecasting is intrinsically a multi-modal problem with substantial uncertainties.
This implies that an ideal motion forecasting method should produce a distribution of future trajectories or at least multiple most likely ones.
Due to the inherent uncertainty, motion forecasting remains challenging and unsolved yet.
Recently, researchers have proposed different architectures based on various representations to encode the kinematic states and context information from HDMap in order to generate feasible multi-modal trajectories (Bansal et al., 2019;
Chai et al., 2019; Gao et al., 2020;
Gu et al., 2021; Liang et al., 2020; Liu et al., 2021; Ngiam et al., 2021; Varadarajan et al., 2021;
Ye et al., 2021; Zeng et al., 2021; Zhao et al., 2020).
These methods follow a traditional static training pipeline, where frames of each scenario are split into historical frames (input) and future frames (ground truth) in a fixed pattern.
Nevertheless, the prediction task is a streaming task in real-world applications, where the current state will become a historical state as time goes by, and the buffer of the historical state is a queue structure to make successive predicted trajectories.
As a result, the temporal consistency thus becomes a crucial requirement for the downstream tasks for fault and noise tolerance.
To tackle this issue, trajectory stitching is widely applied in traditional planning algorithms (Fan et al., 2018) to ensure stability along the temporal horizon.
However, as the trajectory stitching operation is non-differentiable, it cannot be easily incorporated into learning-based models.
Though deep-learning-based models show unprecedented motion prediction performance compared with traditional counterparts, they do not explicitly consider the temporal consistency, leading to unstable behaviors in downstream tasks such as planning.
Inspired by these phenomena, we raise a question: can we explicitly enforce the consistency when training a deep motion prediction model?
On the one hand, the predicted trajectories should be consistent given the successive inputs along the temporal horizon, namely temporal consistency.
On the other hand, the predicted trajectories should be stable and robust against small spatial noise or disturbance, namely spatial consistency.
In this work, we propose a self-supervised scheme to enforce consistency constraints in both spatial and temporal domains, namely Dual Consistency
Constraints.
Our proposed framework, referred as MISC , significantly improves the quality and robustness of motion forecasting, without the need for extra data.
On top of the consistency, multi-modality is another core characteristic of the motion prediction task.
Existing datasets (Chang et al., 2019; Sun et al., 2020) only provide a single ground-truth trajectory for each scenario, which can not satisfy the multi-choice situations such as junction scenarios.
Most methods adopt the winner-takes-all (WTA) (Lee et al., 2016) or its variants (Breuer et al., 2021;
Narayanan et al., 2021) to alleviate this situation.
However, WTA tends to produce confused predictions when two trajectories are very close.
In contrast, our method addresses the multi-modality issue by introducing more powerful teacher targets from self-ensembling.
With self-constraint from multiple soft teacher targets, our model is more likely to be exposed to more high-quality samples, bootstrapping each modality.
Our contributions are summarized as follows,
• We propose Dual Consistency Constraints to enforce temporal and spatial consistency in our model, which is shown to be a general and effective way to improve the overall performance in motion forecasting.
• We propose a self-ensembling constraints training strategy that provides multi-modality supervision explicitly during training to enforce self-consistency with teacher targets.
• We conduct extensive experiments on the Argoverse (Chang et al., 2019) motion forecasting benchmark and our proposed approach achieves the state-of-the-art results.
R ELATED W ORK
Motion Forecasting.
Traditional methods (Houenou et al., 2013; Schulz et al., 2018; Xie et al., 2017; Ziegler et al., 2014) for motion forecasting mainly utilize HDMap information for the prior estimation and Kalman filter (Kalman, 1960) for motion states prediction.
With the recent progress of deep learning on big data, more and more works have been proposed to exploit the potential of data mining in motion forecasting.
Methods (Bansal et al., 2019; Chai et al., 2019; Duvenaud et al., 2015; Gao et al., 2020; Henaff et al., 2015; Liang et al., 2020; Liu et al., 2021; Shuman et al., 2013; Song et al., 2021;
Ye et al., 2021; Zeng et al., 2021) explore different representations, including rasterized image, graph representation, point cloud representation and transformer to generate the features for the task and predict the final output trajectories by regression or post-processing sampling.
Most of these works focus on finding more effective and compact ways of feature extraction on the surrounding environment (HDMap information) and agent interactions.
Based on these representations, other approaches (Casas et al., 2018;
Mangalam et al., 2020; Song et al., 2021;
Zeng et al., 2021; 2019; Zhao et al., 2020) try to incorporate the prior knowledge with traditional methods, which take the predefined candidate trajectories from sampling or clustering strategies as anchor trajectories.
To some extent, these candidate trajectories can provide better guidance and goal coverage for the trajectories regression due to straightforward HDMap encoding.
Nevertheless, this extra dependency makes the stability of models highly related to the quality of the trajectory proposals.
Goal-guided approaches (Gilles et al., 2021; Gu et al., 2021) are therefore introduced to optimize goals in an end-to-end manner, paired with sampling strategies that generate the final trajectory for better coverage rate.
Consistency Regularization.
Consistency Regularization has been fully studied in semi-supervised and self-supervised learning.
Temporally related works (Wang et al., 2019; Lei et al., 2020;
Zhou et al., 2017) have widely explored the idea of cyclic consistency.
Most of the works apply pairwise matching to minimize the alignment difference through optical flow or correspondence matching to achieve temporal smoothness.
Other works (Bachman et al., 2014; F¨oldi´ak, 1991; Ouyang et al., 2021; Sajjadi et al., 2016; Wang et al., 2021) apply consistency constraints to predictions from the same input with different transformations in order to obtain perturbation-invariant representations.
Our work can be seen as a combination of both types of consistency to fully consider the spatial and temporal continuity in motion forecasting.
Multi-hypothesis Learning.
Motion forecasting task inherently has multi-modality due to the future uncertainties and difficulties in acquiring accurate ground-truth labels.
WTA (Guzman-Rivera
[Figure]
et al., 2012; Sriram et al., 2019) in multi-choice learning and its variants (Makansi et al., 2019;
Rupprecht et al., 2017) incorporate with better distribution estimation to improve the training convergence, thus allowing more multi-modality.
Some anchor-based methods (Breuer et al., 2021;
Chai et al., 2019; Phan-Minh et al., 2020; Zeng et al., 2021) introduce pre-defined anchors based on kinematics or road graph topology to provide guidance.
However, these methods only allow one target per training stage.
Other methods (Breuer et al., 2021; Gu et al., 2021) try to generate multi-target for supervision with heavy handcrafted optimizations.
We propose a Teacher-TargetConstraints approach to provide more precise trajectory teacher labels by leveraging the power of self-ensembling (Lee et al., 2013; Zheng et al., 2021).
Multiple targets are explicitly provided to each agent to better model the multi-modality.
A PPROACH
The overall architecture of MISC consists of three parts.
We first utilize a joint spatial and temporal learning framework
TPCN (Ye et al., 2021) to extract pointwise features.
Based on these features, we decouple the trajectory prediction problem as a two-stage regression task.
The first stage performs goal prediction and completes the trajectory with the goal position guidance.
The second stage takes the output of the first stage as anchor trajectories for refinement. 2)
To train our MISC, we propose Dual Consistency Constraints to regularize the predictions both spatially and temporally in a streaming task view.
3) We generate more accurate teacher targets by selfensembling to provide self-consistent Teacher Targets Constraints in Sec. 3.3.
3. A RCHITECTURE
Recently, TPCN (Ye et al., 2021) has gained popularity in this task due to its flexibility for joint spatial-temporal learning and scalability to adopt more techniques from point cloud learning.
Considering its inferiority in representing future uncertainty, we extend TPCN with a two-stage manner through goal position prediction for more accurate waypoints prediction as our baseline.
The whole network is shown in Fig.
Feature Extraction: TPCN utilizes dual-representation point cloud learning techniques with multiinterval temporal learning to model the spatial and temporal relationship.
All the historical trajectories of input agents and map information are based on pointwise representation { p 1 , p 2 , . .
. , p N } , where p i is the i -th point with N points in total, and then go through multi-representation learning framework to generate pointwise features P ∈ R N × C , where C is the channel number.
Goal Prediction: With the pointwise features from the backbone, we also adopt the popular goalbased ideas (Gilles et al., 2021;
Gu et al., 2021; Zhao et al., 2020) to find the optimal planning policy.
Specifically, we first gather all corresponding pointwise agent features and then sum over features to get the agent instance feature ϕ ∈ R 1 × C .
To generate K goal position prediction G = { G k : ( g kx , g ky ) | 1 ≤ k ≤ K } , we use a simple MLP layer:
G = MLP ( ϕ ) .
Instead of relying
[Figure]
Temporal
[Figure]
Trajectory Completion: With the predicted goal positions, we need to complete each trajectory conditioned on these goals.
We propose a simple trajectory completion module to generate K full trajectories (cid:8) τ kreg | 1 ≤ k ≤ K (cid:9) with a single MLP layer as follows:
[Equation]
Trajectory Refinement: Inspired by Faster-RCNN (Ren et al., 2015) and Cascade-RCNN (Cai &
Vasconcelos, 2018), we use the output trajectories from the Trajectory Completion as anchor trajectories to refine trajectories and predict the corresponding possibility of each trajectory.
In particular, the input of the trajectory refinement module will be the whole trajectory with agent historical waypoints τ history .
With a residual block followed by a linear layer Reg and Cls respectively, we regress the delta offset to the first stage outputs ∆ τ reg =
Reg ( τ reg , τ history ) and corresponding scores τ cls = (cid:8) c
k | 1 ≤ k ≤ K (cid:9) respectively, where τ cls =
Cls ( τ reg , τ history ) .
The final output trajectories will be τ reg ′ =
∆ τ reg + τ reg .
3. D UAL C ONSISTENCY C ONSTRAINTS
Consistency regularization has been proved as an effective self-constraint that helps improve robustness against disturbances.
Therefore, we propose Dual Consistency Constraints in both spatial and temporal domains to align predicted trajectories for continuity and stability.
T EMPORAL C ONSISTENCY
In motion forecasting, since each training scenario contains multiple successive frames within a fixed temporal chunk, it is reasonable to assume that any two overlapping chunks of input data with a small time-shift should produce consistent results.
The motion forecasting task aims to predict
K possible trajectories with T time steps for one scenario, given M frames historical information.
Suppose the information at each history frame is I i , where 1 ≤ i ≤ M and the k -th output future trajectories are (cid:8) ( x ki , y ki )
| M < i ≤ M + T (cid:9) .
We first apply time step shift s for the input for temporal consistency.
Therefore, the input history frames information will be { I i |
+ s ≤ i ≤ M + s }
and then we apply the same network for the shifted history information with surrounding HDMap information to generate the k -th output trajectories (cid:8) ( x ′ ki , y ′ ki )
| M + s < i ≤ M + s + T (cid:9) .
When s is small, the driving intentions or behavior keeps stable in a short period.
Since both trajectories have T − s overlapping waypoints, they should be as close as possible and share consensus.
Thus, we can construct self-constraints for a single scenario input due to the streaming property of the input data.
Fig.
2 demonstrates the overall idea of the temporal consistency constraint.
Trajectory Matching:
Since we predict K future trajectories to deal with the multi-modality, it is crucial to consider the trajectory matching relationship between original predictions and time-shifted predictions when applying the temporal consistency alignment.
For a matching problem, the metric on similarity criteria and matching strategies will be two key factors.
Several ways can be used to measure the difference between trajectories, such as Average Displacement Error ( ADE ) and Final
Displacement Error ( FDE ).
We utilize FDE as the criteria since the last position error can partially reflect the similarity with less bias from averaging compared with ADE .
Matching Strategy: There are roughly four ways used for matching, namely forward matching, backward matching, bidirectional matching, and Hungarian matching.
Forward matching takes one trajectory in the current frame and finds its corresponding trajectory in the next frame with the least cost or maximum similarity.
Backward matching is the reverse way compared to forward matching.
Furtherly, bidirectional matching consists of both forward and backward matching, which considers the dual relationship.
Hungarian matching is a linear optimal matching solution based on linear assignment.
Forward and backward matching only considers the one-way situation, which is sensitive to noise and unstable.
Hungarian matching has a high requirement for cost function choice.
Based on these observations, we choose bidirectional matching as our strategy.
We also show its advantages over the other approaches in Sec. 4.3.
After obtaining the optimal matching pairs { ( m k , n k ) | 1 ≤ k ≤ K } , we can compute the consistency constraint by a simple smooth L 1 loss (Ren et al., 2015)
L Huber :
[Equation]
S PATIAL C ONSISTENCY
Since our MISC is a two-stage framework, the second stage mainly aims for trajectory refinement.
It will be more convenient to add spatial permutation in the second stage with less computational cost.
First, we apply spatial permutation function Z , including flipping and random noise, to the trajectories from the first stage.
The refinement module will process these augmented inputs and generate the offset to the ground truth and classification scores.
Under the small spatial permutation and disturbance, we assume that the outputs of the network should also be self-consistent, meaning that the outputs have strong stability or tolerance to noise.
Compared with data augmentation, it is the explicit regularization.
Then the spatial consistency constraint L spa is as follows:
[Equation]
Then the total loss for Dual Consistency Constraints module will be L cons =
L spa + L temp .
3. T EACHER -T ARGET C ONSTRAINTS
Existing datasets (Chang et al., 2019; Sun et al., 2020) only provide a single ground-truth trajectory for the target agent, which is to be predicted in one scenario.
In order to encourage the multimodality of models, the winner-takes-all (WTA) strategy is commonly used to prevent the model from collapsing into a single domain.
However, the WTA training strategy suffers from instability associated with network initialization.
Some other approaches (Breuer et al., 2021; Narayanan et al., 2021) introduce robust estimation methods to select better hypotheses.
To some extent, these methods can only implicitly model the multi-modality.
Some other approaches (Breuer et al., 2021;
Zhao et al., 2020) generate several possible future trajectories based on the kinematics model and road graph topology.
DenseTNT (Gu et al., 2021) only uses teacher labels for goal set prediction through a hill-climbing algorithm.
These optimization methods tend to impose strict constraints and handcrafted prior knowledge, resulting in inaccurate teacher-targets and inferior performance.
In contrast, our approach aims to generate more accurate teacher targets to provide explicit multimodality supervision through self-ensembling to leverage the power of semi-supervised learning.
Teacher-Target Generation.
The key part of our approach lies in generating more accurate teacher labels for each agent.
However, it is straightforward to apply model ensembling techniques (He et al., 2020; Laine & Aila, 2016; Tarvainen & Valpola, 2017) to obtain more powerful predictions.
Compared with previous works (Breuer et al., 2021; Chai et al., 2019; Zhao et al., 2020), we do not rely on handcrafted anchor trajectory sampling, which is based on inaccurate prior knowledge, including motion estimation.
Meanwhile, soft targets from ensembling can better finetune the predictions and reduce the gradient variance for better training convergence.
As suggested in works (Dietterich, 2000; Opitz & Maclin, 1999), the prediction error decreases when the ensemble approach is used
[Figure]
once the model is diverse enough.
Therefore, we apply k-means algorithm (MacQueen et al., 1967) to the predicted trajectories that are collected within different training procedures (for example, launched with different seeds of random number generators, optimized with different learning rates, etc.) of MISC without Teacher-Target Constraints to generate J trajectories with corresponding scores for each scenario.
Fig.
3 shows the overall process of our approach.
Then with the original ground-truth label, we will formulate J + 1 target trajectories as follows:
[Equation]
where τ jtgt is the j -th trajectory with score c j , among J + 1 target trajectories.
To simplify the notation, τ 0 tgt is the ground-truth trajectory with c 0 set to 1 .
3. L EARNING
The total supervision of our MISC can be decoupled into several parts, as described in previous sections.
For the regression and classification parts, we loop over all the possible J + 1 targets τ tgt .
For each target τ jtgt with confidence τ jconf , we apply WTA strategy as described in Sec. 3.3.
Suppose k ∗ -th trajectory from trajectory refinement output τ reg ′ is the best trajectory which has the maximum similarity with target τ jtgt , the classification loss and regression loss are defined as
[Equation]
For classification loss design, we adopt the displacement prediction idea from TPCN
(Ye et al., 2021) to alleviate the hard assignment phenomenon.
As for converting the displacement into probability, we use the standard softmin function to distribute the scores.
Since we have trajectory completion and refinement modules, the regression loss will be L reg =
(cid:80)
Jj =
( L jreg + L j ∆ reg ) , where L j ∆ reg is the regression loss for the refinement module.
The final loss is L =
L reg + L cls + L cons .
E XPERIMENTS
We conduct experiments on the Argoverse dataset (Chang et al., 2019), one of the largest publicly available motion forecasting datasets.
We compare our MISC with other state-of-the-art methods.
Furthermore, we provide ablation studies to evaluate the effectiveness and generalization ability of each proposed module and design experiments for some hyperparameter choices.
[Table]
4. E XPERIMENTAL S ETUP
Dataset.
Argoverse (Chang et al., 2019) is currently one of the most popular motion forecasting datasets.
It provides more than 300K scenarios with rich HDMap information.
For each scenario, objects are divided into three types: agent, AV and others, where “agent” is the object to be predicted.
Moreover, each scenario contains 50 frames sampled at 10 Hz, meaning that the time interval between successive frames is 0 .
1 s. The whole dataset is split into training, validation, and test sets, with 205942 , 39472 , and 78143 sequences, respectively.
Metrics.
We use the standard evaluation metrics, including ADE and FDE.
ADE is defined as the average displacement error between ground-truth trajectories and predicted trajectories over all time steps.
FDE is defined as displacement error between ground-truth trajectories and predicted trajectories at the last time step.
We predict K candidate trajectories for each scenario and calculate the metrics with the ground truth labels.
Accordingly, minADE and minFDE are minimum ADE and FDE over the top K predictions.
Moreover, miss rate (MR) is also considered, defined as the percentage of the best-predicted trajectories whose FDE is within a threshold ( 2 m).
Brier-minFDE is the minFDE plus (1 − p ) 2 , where p is the corresponding trajectory probability.
Metrics for K =and K = 6 are used in our experiments.
Note that Brier-minFDE 6 is the ranking metric.
Experimental Details.
We apply some data augmentation, including random flipping with a probability 0 .
5 and global random scaling with the scaling ratio between [0 . 8 , 1 . 25] during the training stage.
As for model settings, the time shift s for the temporal consistency constraint is set to 1 .
We adopt K = 6 to generate 6 trajectories and use J = 6 teacher targets for each scenario.
Furthermore, we choose bidirectional-matching for temporal consistency constraint.
We finally use 10 models for ensembling due to computation resource limits.
For more training details, we have included them in the supplementary materials.
4. E XPERIMENTAL R ESULTS
Argoverse Leaderboard Results.
We provide detailed quantitative results of our MISC on the
Argoverse test set as well as public state-of-the-art methods in Tab. 1.
Compared with previous methods, our MISC improves all the evaluation metrics except MR 6 by a large margin.
Furtherly, since the proposed modules are all general training components, other existing motion forecasting models can also benefit greatly from these strategies.
Qualitative Results.
We also present some qualitative results on the Argoverse validation set in
Fig.
Compared with results without consistency, the Dual Consistency Constraints improve both the quality and smoothness of the predicted trajectories significantly, resulting in more feasible and stable results despite the input noise.
4. A BLATION S TUDIES
Component Study.
As shown in Tab. 2, we conduct an ablation study for our MISC on the Argoverse validation set to evaluate the effectiveness of each proposed component.
We adopt TPCN (Ye
[Figure]
[Figure] [Figure]
Consistency
[Table]
et al., 2021) as the baseline shown in the first row of Tab. 2 and add the proposed components progressively.
The architecture modifications from the goal set prediction and trajectory refinement module show their promising improvements of about 2 %.
Dual consistency Constraints have the largest improvements of more than 5 % among all the evaluation metrics.
Especially for minFDE 1 , temporal consistency can optimize 20 cm, indicating the temporal constraints can improve both final position and trajectory probability prediction.
Compared with temporal consistency, spatial consistency has less effect on models since we only enforce this constraint in the trajectory refinement stage.
Finally, the Teacher-Target Constraints significantly increases performance, manifesting its effectiveness in helping training convergence.
Temporal Consistency Factors.
We study the factors in the matching problems, including similarity and matching strategies.
As shown in Tab. 3, both Hungarian and Bidirectional matching show their advantages over the single direction matching.
Although Hungarian matching can ensure the study on matching factor for temporal consistency.
In this experiment, we remove the Teacher-Target Constraints to fairly study the effect
[Table] [Table]
[Table]
one-to-one matching relationship, it is sensitive to the similarity metric and numerical precision, both of which are not stable in the early training stage.
In contrast, bidirectional matching with the
FDE similarity metric nearly achieves the best results across all the evaluation metrics.
Meanwhile, we also conduct experiments to find the best time-shift value s in the temporal consistency.
The details can be found in appendix 6.
Number of Teacher Targets.
As shown in Tab. 4, more teacher targets could bring better performance.
Compared with J = 1 , 6 teacher targets bring an extra nearly 1% improvements.
However, the marginal improvement decreases significantly so we finally choose J = 6 .
4. G ENERALIZATION C APABILITY
To verify the generalization capability of Dual Consistency Constraints and Teacher Targets Constraints, we also apply them to different models with state-of-the-art performance to show that they can be plugin-in training schemes.
Consistency Component.
As shown in Tab. 5, our dual consistency constraints can effectively improve the performance of models regardless of their representations through the training phase.
There is a noticeable improvement of over 5% on every metric, especially for minFDE.
Teacher Target.
Teacher-Target Constraints is another general training trick that can be widely used in other frameworks.
In Tab. 5, we also verify its effectiveness on other public methods.
Methods with Teacher-Target Constraints have nearly over 3 % improvement in all metrics.
For the original DenseTNT (Gu et al., 2021), we replace its original handcrafted optimization for teacher goal targets with our self-ensembling teacher targets.
This strategy brings an over 5 % increase in performance, demonstrating the better quality of the self-ensembling teacher targets than handcrafted optimizations and estimation.
C ONCLUSION
In this work, we propose MISC, an effective architecture for the motion forecasting task that explicitly models the multi-modality.
We also impose dual consistency regularization on both spatial and temporal domains to leverage the potential of self-supervision, which has been ignored by previous efforts.
Besides, we explicitly model the multi-modality by providing supervision with powerful self-ensembling techniques.
Experimental results on the Argoverse motion forecasting dataset show the effectiveness of our approach and generalization capability to other methods.
R EPRODUCIBILITY S TATEMENT
We use the publicly available Argoverse Dataset (Chang et al., 2019) available at https://www.
argoverse.org/av1.html#forecasting-link .
Dataset preprocessing is shown in 4.1.
Training process is in Appendix A.2.
And the model architecture is illustrated in the Sec.
3.1 and
Appendix A.1.
A PPENDIX A. M ODEL D ETAILS
We provide the detailed network architecture of our MISC in Fig.
We use TPCN (Ye et al., 2021) as our backbone.
The feature extraction consists of 4 spatial modules and 4 dynamic temporal learning layers same as TPCN.
Before the prediction header, we calculate the mean features and remove map instances features.
For the spatial module, the point representation utilizes PointNet++ (Qi et al., 2017) with neighborhood radius of [0 . 2 m, 0 .
4 m, 0 .
8 m ] , while the voxel representation uses
Sparse BottleNeck.
We use all the points in this process without any sampling.
More details about backbone can be found in TPCN (Ye et al., 2021).
A. T RAINING D ETAILS
We train MISC for 50 epochs using a batch size of 32 with Adam (Kingma & Ba, 2014) optimizer with an initial learning rate of 0 .
001 , which is decayed every 15 epochs in a ratio of 0 . 1 .
[Figure]
A. A BLATION STUDY A.3. T EMPORAL CONSISTENCY
Meanwhile, we also conduct experiments to find the best time-shift value s in the temporal consistency.
As shown in Tab. 6, choosing time shift s = 1 has already achieved decent performance, with five out of six metrics ranking the first.
Further increasing the s will not bring much performance gain since the driving behavior could change a lot with large s .
We use the average L2 distance among all predicted trajectory waypoints to measure the temporal consistency.
As shown in Fig.
6, our model without temporal consistency will have large inconsistency even though the time shift s is small, which may lead to unstable behavior for the downstream
[Table]
task such as planning.
With temporal consistency constraints, there is a significant improvement for the L2 distance divergence, demonstrating the effectiveness of our method.
A.3. S PATIAL CONSISTENCY
Furthermore
[Table]
we also measure the spatial inconsistency against flipping and Gaussian noise with zero mean and standard deviation of 15 cm.
The average spatial inconsistency will be 19 .
3 cm
[Table]
while the number decreases to 10 . 2 cm with our spatial consistency constraint.
A.3. C OMPONENT S TUDY
We provide a controlled experiment to verify the effectiveness of the proposed method when turning both Dual Consistency Constraints and Teacher-Target Constraints on at the same time shown in
Tab. 7.
With both modules on, the performance of all the methods benefits a lot, about nearly 7% , demonstrating the generalization capability and effectiveness of our approach.
It also shows that these two modules can be independently helpful.
A.3. R ESULTS ON W AYMO D ATASET
[Table] [Table]
We provide some quantitative results on the validation set of the Waymo Open dataset motion prediction task (Ettinger et al., 2021), shown in Tab. 8.
Compared with KEMP (Lu et al., 2022) and
SceneTransformer (Ngiam et al., 2021), we also achieve very promising results and show comparable improvement, demonstrating the effectiveness of our approach
[Table]
A. M ODEL COMPLEXITY
[Table]
We provide detailed runtime speed evaluated in a single RTX2080Ti with the model parameters shown in Tab. 9.
Compared with other state-of-the-art models, we achieve decent performance without introducing more computation cost
. A. Q UALITATIVE A NALYSIS
We provide some visual results of MISC on the the Argoverse
(Chang et al., 2019) validation set in
Fig.
8 as well as the Argoverse test set in Fig.
These qualitative results demonstrate the effectiveness and the high-quality predicted trajectories of our method.
A. F AILURE C ASES
We also present some failure cases on the validation set in Fig.
Some possible reasons are:
• The ground-truth labels contain some noises.
Since the ground-truth labels are obtained from tracking, there may be some id switches, leading to the sudden perturbation of the agents’ location (e
g., the first and third example in the second row of Fig. 7).
Under these scenarios, the predicted trajectories from MISC are more reasonable and stable without large jerks.
• The multi-modality problem.
In some situations, MISC can not predict the intention perfectly without enough motion and map information.
The first and third example in the first row of Fig.
7 demonstrate this phenomenon.
The agent makes a lane change decision without many hints in the historical information.
Thus, this can be furtherly improved by introducing more map constraints.