Parking is a critical pillar of driving safety. While recent end-to-end (E2E) approaches have achieved promising in-domain results, robustness under domain shifts (e.g., weather and lighting changes) remains a key challenge. Rather than relying on additional data, in this paper, we propose Dino-Diffusion Parking (DDP), a domain-agnostic autonomous parking pipeline that integrates visual foundation models with diffusion-based planning to enable generalized perception and robust motion planning under distribution shifts. We train our pipeline in CARLA at regular setting and transfer it to more adversarial settings in a zero-shot fashion. Our model consistently achieves a parking success rate above 90% across all tested out-of-distribution (OOD) scenarios, with ablation studies confirming that both the network architecture and algorithmic design significantly enhance cross-domain performance over existing baselines. Furthermore, testing in a 3D Gaussian splatting (3DGS) environment reconstructed from a real-world parking lot demonstrates promising sim-to-real transfer.

This paper presents a Segmented Trajectory Optimization (STO) method for autonomous parking, which refines an initial trajectory into a dynamically feasible and collision-free one using an iterative SQP-based approach. STO maintains the maneuver strategy of the high-level global planner while allowing curvature discontinuities at switching points to improve maneuver efficiency. To ensure safety, a convex corridor is constructed via GJK-accelerated ellipse shrinking and expansion, serving as safety constraints in each iteration. Numerical simulations in perpendicular and reverse-angled parking scenarios demonstrate that STO enhances maneuver efficiency while ensuring safety. Moreover, computational performance confirms its practicality for real-world applications.

--While traditional autonomous driving methods with multi-stage pipelines suffer from lengthy processes, error accumulations and maintenance difficulties, the end-to-end method is designed to map the data of multiple sensors directly into motion control commands, with high flexibility, efficiency and generalization. Therefore, the end-to-end model has shown great potential in autonomous driving. Due to the low speed, low risk, and low complexity characteristics of autonomous parking scenarios, end-to-end methods can be applied to autonomous parking systems earlier . While prior work introduced a visual-based parking model and a pipeline for data generation, training and closed-loop test, the dataset itself was not released. T o bridge this gap, we work on creating large end-to-end autonomous parking datasets in CARLA based on the prior work'E2E Parking'. Keyboard control is replaced by Handle Controller to improve usability, efficiency, and operational precision. During the iterative process of dataset generation, we evaluate the effect of different factors on the parking performance of the controlled vehicle, including diverse scenes generated by multiple random seeds, the position of the roadside object's shadow dependent on weather setting, dataset size, initial learning rate and training epochs. We recommend generating at least 2 scenes for each parking slot with different random seeds, where 8 trajectories with different initial positions are collected for each scene. Weather settings should be modified to make the dataset include scenes with shadow projected on the target slot. Experiments demonstrate that an initial learning rate of 7. 5 10 After several iterations, we are able to open-source a high-quality dataset for end-to-end autonomous parking.

Autonomous parking plays a vital role in intelligent vehicle systems, particularly in constrained urban environments where high-precision control is required. While traditional rule-based parking systems struggle with environmental uncertainties and lack adaptability in crowded or dynamic scenes, human drivers demonstrate the ability to park intuitively without explicit modeling. Inspired by this observation, we propose a Transformer-based end-to-end framework for autonomous parking that learns from expert demonstrations. The network takes as input surround-view camera images, goal-point representations, ego vehicle motion, and pedestrian trajectories. It outputs discrete control sequences including throttle, braking, steering, and gear selection. A novel cross-attention module integrates BEV features with target points, and a GRU-based pedestrian predictor enhances safety by modeling dynamic obstacles. We validate our method on the CARLA 0.9.14 simulator in both vertical and parallel parking scenarios. Experiments show our model achieves a high success rate of 96.57\%, with average positional and orientation errors of 0.21 meters and 0.41 degrees, respectively. The ablation studies further demonstrate the effectiveness of key modules such as pedestrian prediction and goal-point attention fusion. The code and dataset will be released at: https://github.com/little-snail-f/ParkFormer.

This paper proposed a novel method for autonomous parking. Autonomous parking has received a lot of attention because of its convenience, but due to the complex environment and the non-holonomic constraints of vehicle, it is difficult to get a collision-free and feasible path in a short time. To solve this problem, this paper introduced a novel algorithm called Multi-Heuristic Hybrid A* (MHHA*) which incorporates the characteristic of Multi-Heuristic A* and Hybrid A*. So it could provide the guarantee for completeness, the avoidance of local minimum and sub-optimality, and generate a feasible path in a short time. And this paper also proposed a new collision check method based on coordinate transformation which could improve the computational efficiency. The performance of the proposed method was compared with Hybrid A* in simulation experiments and its superiority has been proved.

Automakers are fond of experimenting with self-parking cars, but VW intends to make it a practical reality. It's promising that vehicles in the company group (which includes brands like Audi, Bentley, Porsche and Lamborghini) will start including autonomous parking as of 2020. The system will only be available in "selected" parking garages at first, but it relies on pictorial guiding markers that are theoretically usable in any garage. The system will arrive in two phases. At first, cars with autonomous parking will be guided to a separate area so they don't have to deal with the unpredictability of human drivers.

Plenty of cars will help you park, but the biggest challenge is frequently finding a spot in the first place -- it's no fun to circle the parking lot for 10 minutes. Fully autonomous cars can ultimately take care of this, but Jaguar Land Rover is demonstrating a feature that would help in the meantime. It recently expanded its public semi-autonomous testing in the UK to include a "self-driving valet" where vehicles both find open spaces and park themselves. The company pitches it as eliminating some of the drudgery of driving, letting you take the wheel when you'd genuinely enjoy it. The automaker has also been testing other connected car features, including a collaborative parking feature where vehicles share info about free spaces as you approach a parking lot.