Organizations: Jishu Miao, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi are with Chubu University, Kasugai, Japan. · Han Chen is with Tongji University, Shanghai, China. · Jiankun Zhai, Qi Liu are independent researchers.
Autonomous parking fundamentally differs from on-road driving due to its frequent direction changes and complex maneuvering requirements. However, existing End-to-End (E2E) planning methods often simplify the parking task into a geometric path regression problem, neglecting explicit modeling of the vehicle's kinematic state. This "dimensionality deficiency" easily leads to physically infeasible trajectories and deviates from real human driving behavior, particularly at critical gear-shift points in multi-shot parking scenarios. In this paper, we propose SunnyParking, a novel dual-branch E2E architecture that achieves motion state awareness by jointly predicting spatial trajectories and discrete motion state sequences (e.g., forward/reverse). Additionally, we introduce a Fourier feature-based representation of target parking slots to overcome the resolution limitations of traditional bird's-eye view (BEV) approaches, enabling high-precision target interactions. Experimental results demonstrate that our framework generates more robust and human-like trajectories in complex multi-shot parking scenarios, while significantly improving gear-shift point localization accuracy compared to state-of-the-art methods. We open-source a new parking dataset of the CARLA simulator, specifically designed to evaluate full prediction capabilities under complex maneuvers.
Figures & tables
Fig. 1 : Comparison of parking planning paradigms. Geometric Plan represents many current E2E approaches by focusing primarily on geometric features. Modular Plan represents the traditional rule-based paradigm (e.g., Hybrid A*). While it generates a kinematic feasible plan, achieving human-like planning remains a challenge. Our Unified Kinematic Plan achieves comparable feasibility through explicit kinematic modeling, while demonstrating superior “Human-like” qualities via its learning-based approach.
Fig. 2 : The Overiew of SunnyParking’s Network Architecture . It takes 4 RGB images as the sensory input, along side it is the selected target slot. Vision Encoder extracts the vision-domain features then project them to the BEV space as the spatial features. Fourier Mapping Encoder first represents the target including it’s (x,y,θ) in Fourier feature, then query the spatial features. In YUKUI module: Trajectory Branch takes the fused features then predicts a set of waypoints. With the additional input from Trajectory Branch’s hidden layer, Motion State Branch predicts the motion state, corresponding with each points
Fig. 3 : The Architecture of Motion State Branch. Motion Query is enriched by the trajectory feature via cross-attention. This resulting the fused Queries is then input to a Transformer Decoder.
Fig. 4 : Progressive transition from teacher forcing to autoregressive prediction via Scheduled Sampling.
TABLE I: Performance Evaluation of Trajectory and Gear Shift Points.
Set
Input Rep.
Model Comp.
Sched. Sampling
Trajectory Metrics
Motion State Metrics
BEV
Fourier
Traj.
Motion
L2 Dis.
Four. Diff.
Haus. Dis.
AHE
Acc.
Avg. Dist.
1
✓
✓
3.505
40.033
3.760
/
/
/
2
✓
✓
✓
2.327
39.492
2.408
/
/
/
3
✓
✓ext
✓
1.474
27.253
1.635
9.8
0.827
1.900
4
✓
✓ext
✓
✓
1.800
37.253
2.160
11.7
0.794
2.385
5
✓
✓
2.863
55.885
3.172
/
/
/
TABLE II : Ablation Study of Different Model Components.
Fig. 5 : Qualitative Comparison of Parking Planning Results. Blue represents the GT trajectory, while red indicates the prediction. As task complexity increases, baseline methods struggle: TransFuser fails to generate feasible trajectories, while ParkingE2E exhibits overfitting to specific parking patterns. Our method produces robust trajectories with correctly predicted gear shift points. Notably, as shown in ( 5(o) ), our method demonstrates superior feature representation capabilities. It effectively handles regions near the boundaries of the BEV FOV, comparable to the direct 2D coordinate input strategy used in TransFuser.
Fig. 6 : Comparison of Attention Heatmaps between Baseline (BEV) and Ours (Fourier). Our method exhibits an attention shift as the relative position between the ego rear and the target slot changes.
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Fig. 7 : Data Collection Scenario of the Parking Lot in BEV. The orange boxes represent the 16 target slots, while the blue spots are the start points for a specific task T (i.e., “task 11”). The ego vehicle pose is determined by ( 24 ).
Fig. 8 : A human driver is performing a parking maneuver.
Sensor
Count
Resolution
FOV
RGB Camera
4
400×300 px
100∘
Depth Camera
4
400×300 px
100∘
IMU
1
Accelerometer (3-axis),
Gyroscope (3-axis), Compass
GNSS
1
Provides Latitude, Longitude
Vehicle State
–
Position, Orientation, Speed,
Appendix
TABLE III : Consolidated Sensor Specifications
Fig. 9 : Distribution Map of Parking Start Point. All target parking slots are normalized to the center of the image. It can be seen that for parking in one side, there are 20 start points (2 columns by 10 rows), accompanied by a certain amount of jitter.
Fig. 10 : Overview of data distributions for core metrics, grouped by the number of gear shifts. From left to right: Average Speed, Slice Length, and Trajectory Length.
Category
Avg. Speed
Slice Len.
Traj. Len.
Count
(km/h)
(frames)
(m)
1 -shot
Unconstrained
25
2 -shot
[2.90,6.70]
[101,210]
[12.8,25.2]
111
3 -shot
[2.79,5.70]
[125,219]
[14.0,22.5]
120
4 -shot
[2.83,6.00]
[144,280]
[17.0,31.0]
306
Total
562
Appendix
TABLE IV : Data Filtering Thresholds and Final Task Distribution
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China · School of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China · College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China +1