Motion forecasting represents a critical challenge in autonomous driving systems, requiring accurate prediction of surrounding agents’ future trajectories. While existing approaches predict future motion states with the extracted scene context feature from historical agent trajectories and road layouts, they suffer from the information degradation during the scene feature encoding. To address the limitation, we propose HAMF, a novel motion forecasting framework that learns future motion representations with the scene context encoding jointly, to coherently combine the scene understanding and future motion state prediction. We first embed the observed agent states and map information into 1D token sequences, together with the target multi-modal future motion features as a set of learnable tokens. Then we design a unified Attention-based encoder, which synergistically combines self-attention and cross-attention mechanisms to model the scene context information and aggregate future motion features jointly. Complementing the encoder, we implement the Mamba module in the decoding stage to further preserve the consistency and correlations among the learned future motion representations, to generate the accurate and diverse final trajectories. Extensive experiments on Argoverse 2 benchmark demonstrate that our hybrid Attention-Mamba model achieves state-of-the-art motion forecasting performance with the simple and lightweight architecture.

Accurate prediction of pedestrian trajectories is crucial for improving the safety of autonomous driving. However, this task is generally nontrivial due to the inherent stochasticity of human motion, which naturally requires the predictor to generate multi-modal prediction. Previous works leverage various generative methods, such as GAN and VAE, for pedestrian trajectory prediction. Nevertheless, these methods may suffer from mode collapse and relatively low-quality results. The denoising diffusion probabilistic model (DDPM) has recently been applied to trajectory prediction due to its simple training process and powerful reconstruction ability. However, current diffusion-based methods do not fully utilize input information and usually require many denoising iterations that lead to a long inference time or an additional network for initialization. To address these challenges and facilitate the use of diffusion models in multi-modal trajectory prediction, we propose GDTS, a novel Goal-Guided Diffusion Model with Tree Sampling for multi-modal trajectory prediction. Considering the "goal-driven" characteristics of human motion, GDTS leverages goal estimation to guide the generation of the diffusion network. A two-stage tree sampling algorithm is presented, which leverages common features to reduce the inference time and improve accuracy for multi-modal prediction. Experimental results demonstrate that our proposed framework achieves comparable state-of-the-art performance with real-time inference speed in public datasets.

3D lane detection is essential in autonomous driving (AD) as it extracts structural and traffic information from the road in 3D space, aiding autonomous vehicles in logical, safe, and comfortable path planning and motion control. Given the cost of sensors and the advantages of visual data in color information, 3D lane detection based on monocular vision is an important research direction in the realm of AD that increasingly gains attention in both industry and academia. Nevertheless, recent advancements in visual perception seem inadequate for the development of fully reliable 3D lane detection algorithms, which also hampers the progress of vision-based fully autonomous vehicles. We believe that it still leaves an open and interesting problem for improvement in 3D lane detection algorithms for autonomous vehicles using visual sensors, and significant enhancements are essentially required. This review summarizes and analyzes the current state of achievements in the field of 3D lane detection research. It covers all current monocular-based 3D lane detection processes, discusses the performance of these cutting-edge algorithms, analyzes the time complexity of various algorithms, and highlights the main achievements and limitations of ongoing research efforts. The survey also includes a comprehensive discussion of available 3D lane detection datasets and the challenges that researchers encounter but have not yet resolved. Finally, our work outlines future research directions and invites researchers and practitioners to join this exciting field.

Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two key stages: self-supervised learning (SSL) and feature distillation. POP first employs SLL to help the model learn to reconstruct history representations, and then utilizes feature distillation as the fine-tuning task to transfer knowledge from the teacher model, which has been pre-trained with complete observations, to the student model, which has only few observations. POP achieves comparable results to topperforming methods in open-loop experiments and outperforms the baseline method in closed-loop simulations, including safety metrics. Qualitative results illustrate the superiority of POP in providing reasonable and safe trajectory predictions. Demo videos and code are available at https://chantsss.github.io/POP/.

Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mapping system that utilizes multiple Truncated Signed Distance Field (TSDF) submaps and panoptic labels to hierarchically model the environment. The output map is able to maintain both voxel- and submap-level metric and semantic information. Two modules are presented to enhance the mapping efficiency and label consistency: (1) an inter-submaps label fusion strategy to eliminate duplicate points across submaps and (2) a conditional random field (CRF) based approach to enhance panoptic labels. We conducted experiments with two public datasets including indoor and outdoor scenarios. Our system performs comparably to state-of-the-art (SOTA) methods across geometry and label accuracy evaluation metrics. The experiment results highlight the effectiveness and scalability of our system, as it is capable of constructing precise geometry and maintaining consistent panoptic labels. Our code is publicly available at https://github.com/hutslib/DHP-Mapping.

Evaluating and training autonomous driving systems require diverse and scalable corner cases. However, most existing scene generation methods lack controllability, accuracy, and versatility, resulting in unsatisfactory generation results. Inspired by DragGAN in image generation, we propose DragTraffic, a generalized, interactive, and controllable traffic scene generation framework based on conditional diffusion. DragTraffic enables non-experts to generate a variety of realistic driving scenarios for different types of traffic agents through an adaptive mixture expert architecture. We employ a regression model to provide a general initial solution and a refinement process based on the conditional diffusion model to ensure diversity. User-customized context is introduced through cross-attention to ensure high controllability. Experiments on a real-world driving dataset show that DragTraffic outperforms existing methods in terms of authenticity, diversity, and freedom. Demo videos and code are available at https://chantsss.github.io/Dragtraffic/.

This paper presents a generic trajectory planning method for wheeled robots with fixed steering axes while the steering angle of each wheel is constrained. In the existing literatures, All-Wheel-Steering (AWS) robots, incorporating modes such as rotation-free translation maneuvers, in-situ rotational maneuvers, and proportional steering, exhibit inefficient performance due to time-consuming mode switches. This inefficiency arises from wheel rotation constraints and inter-wheel cooperation requirements. The direct application of a holonomic moving strategy can lead to significant slip angles or even structural failure. Additionally, the limited steering range of AWS wheeled robots exacerbates non-linearity characteristics, thereby complicating control processes. To address these challenges, we developed a novel planning method termed Constrained AWS (C-AWS), which integrates second-order discrete search with predictive control techniques. Experimental results demonstrate that our method adeptly generates feasible and smooth trajectories for C-AWS while adhering to steering angle constraints. Code and video can be found at https://github.com/Rex-sys-hk/AWSPlanning.

Considerable research efforts have been devoted to the development of motion planning algorithms, which form a cornerstone of the autonomous driving system (ADS). Nonetheless, acquiring an interactive and secure trajectory for the ADS remains challenging due to the complex nature of interaction modeling in planning. Modern planning methods still employ a uniform treatment of prediction outcomes and solely rely on collision-avoidance strategies, leading to suboptimal planning performance. To address this limitation, this paper presents a novel prediction-based interactive planning framework for autonomous driving. Our method incorporates interaction reasoning into spatio-temporal (s-t) planning by defining interaction conditions and constraints. Specifically, it records and continually updates interaction relations for each planned state throughout the forward search. We assess the performance of our approach alongside state-of-the-art methods in the CommonRoad environment. Our experiments include a total of 232 scenarios, with variations in the accuracy of prediction outcomes, modality, and degrees of planner aggressiveness. The experimental findings demonstrate the effectiveness and robustness of our method. It leads to a reduction of collision times by approximately 17.6% in 3-modal scenarios, along with improvements of nearly 7.6% in distance completeness and 31.7% in the fail rate in single-modal scenarios. For the community’s reference, our code is accessible at https://github.com/ChenYingbing/IR-STP-Planner.

Drivable area segmentation is an essential component of the visual perception system for autonomous driving vehicles. Recent efforts in deep neural networks have sig-nificantly improved semantic segmentation performance for autonomous driving. However, most DNN-based methods need a large amount of data to train the models, and collecting large-scale datasets with manually labeled ground truth is costly, tedious, time consuming and requires the availability of experts, making DNN-based methods often difficult to implement in real world applications. Hence, in this paper, we introduce a novel module named automatic data labeler (ADL), which leverages a deterministic LiDAR-based method for ground plane segmentation and road boundary detection to create large datasets suitable for training DNNs. Furthermore, since the data generated by our ADL module is not as accurate as the manually annotated data, we introduce uncertainty estimation to compensate for the gap between the human labeler and our ADL. Finally, we train the semantic segmentation neural networks using our automatically generated labels on the KITTI dataset [10] and KITTI-CARLA dataset [7]. The experimental results demonstrate that our proposed ADL method not only achieves impressive performance compared to manual labeling but also exhibits more robust and accurate results than both traditional methods and state-of-the-art self-supervised methods.

Our goal is to train a neural planner that can capture diverse driving behaviors in complex urban scenarios. We observe that even state-of-the-art neural planners are struggling to perform common maneuvers such as lane change, which is rather natural for human drivers. We propose to explore the multi-modalities in the planning problem and force the neural planner to explicitly consider different policies. This is achieved by generating the future trajectories conditioned on every possible reference line, which could simply be the centerline of the surrounding lanes. We find this simple strategy yet enables the planner to perform rich and complex behaviors. We train our model using real-world driving data and demonstrate the effectiveness of our method through both open-loop and closed-loop evaluations. Project website https://jchengai.github.io/mpnp.