Interactive behavior is important for autonomous driving vehicles, especially for scenarios like ramp merging which require significant social interaction between autonomous driving vehicles and human-driven cars. This paper enhances our previous Probabilistic Graphical Model (PGM) merging control model for the interactive behavior of autonomous driving vehicles. To better estimate the interactive behavior for autonomous driving cars, a Factor Graph (FG) is used to describe the dependency among observations and estimate other cars’ intentions. Real trajectories are used to approximate the model instead of human-designed models or cost functions. Forgetting factors and a Gaussian Mixture Model (GMM) are also applied in the intention estimation process for stabilization, interpolation and smoothness. The advantage of the factor graph is that the relationship between its nodes can be described by self-defined functions, instead of probabilistic relationships as in PGM, giving more flexibility. Continuity of GMM also provides higher accuracy than the previous discrete speed transition model. The proposed method enhances the overall performance of intention estimation, in terms of collision rate and average distance between cars after merging, which means it is safer and more efficient.

Modern high-definition LIDAR is expensive for commercial autonomous driving vehicles and small indoor robots. An affordable solution to this problem is fusion of planar LIDAR with RGB images to provide a similar level of perception capability. Even though state-of-the-art methods provide approaches to predict depth information from limited sensor input, they are usually a simple concatenation of sparse LIDAR features and dense RGB features through an end-to-end fusion architecture. In this paper, we introduce an inductive late-fusion block which better fuses different sensor modalities inspired by a probability model. The proposed demonstration and aggregation network propagates the mixed context and depth features to the prediction network and serves as a prior knowledge of the depth completion. This late-fusion block uses the dense context features to guide the depth prediction based on demonstrations by sparse depth features. In addition to evaluating the proposed method on benchmark depth completion datasets including NYUDepthV2 and KITTI, we also test the proposed method on a simulated planar LIDAR dataset. Our method shows promising results compared to previous approaches on both the benchmark datasets and simulated dataset with various 3D densities.

Interactive driving is challenging but essential for autonomous cars in dense traffic or urban areas. Proper interaction requires understanding and prediction of future trajectories of all neighboring cars around a target vehicle. Current solutions typically assume a certain distribution or stochastic process to approximate human-driven cars' behaviors. To relax this assumption, a Recurrent Meta Induction Network (RMIN) framework is developed. The original Conditional Neural Process (CNP) on which this is based does not consider the sequence of the conditions, due to the permutation invariance requirements for stochastic processes. However, the sequential information is important for the driving behavior estimation. Therefore, in the proposed method, a recurrent neural cell replaces the original demonstration sub-net. The behavior estimation is conditioned on the historical observations for all related cars, including the target car and its surrounding cars. The method is applied to predict the lane change trajectory of a target car in dense traffic areas. The proposed method achieves better results than previous methods and thanks to the meta-learning framework, it can use a smaller dataset, putting fewer demands on autonomous driving data collection.

Performing safe and efficient lane changes is a crucial feature for creating fully autonomous vehicles. Recent advances have demonstrated successful lane following behavior using deep reinforcement learning, yet the interactions with other vehicles on-road for lane changes are rarely considered. In this paper, we design a hierarchical Deep Reinforcement Learning (DRL) algorithm to learn lane change behaviors in dense traffic. By breaking down overall behavior to sub-policies, faster and safer lane change actions can be learned. We also apply temporal and spatial attention to the DRL architecture, which helps the vehicle focus more on surrounding vehicles and leads to smoother lane change behavior. We conduct our experiments in the TORCS simulator and the results outperform the state-of-the-art deep reinforcement learning algorithm in various lane change scenarios.

Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also man- age these situations by behaving cooperatively and naturally. In this paper, we enhance our previous learning-based method to efficiently estimate other vehicles' intentions and interact with them in ramp merging scenarios, without over-the-air communication between vehicles. The proposed approach inherits our previous Probabilistic grahpical Model (PGM) and distance- keeping framework. Real driving trajectories are used to learn transition models in the PGM. Thus, besides the structure of the PGM, our method does not require human-designed reward or cost functions. The PGM-based intention estimation is followed by an off-the-shelf distance-keeping model to generate proper acceleration/deceleration controls. The PGM plays a plug-in role in our self-driving framework. The new model eliminates two assumptions in the previous model: 1) a fixed merging point for all merging agents, which is hard to determine before the merging vehicles make the merge; 2) Perfect velocity measurement, which requires sophisticated perception systems. We validate the performance of our method both on real merging data and using a designed merging strategy in simulation, and show significant improvements compared with previous methods. Parameter design is also discussed by experiments. The new method is computationally efficient, and exhibits better robustness against sensing uncertainty.

Nowadays, self-driving cars are being applied to more complex urban scenarios including intersections, merging ramps or lane changes. It is, therefore, important for self-driving cars to behave socially with human-driven cars. In this paper, we focus on generating the lane change behavior for self-driving cars: perform a safe and effective lane change behavior once a lane-change command is received. Our method bridges the gap between higher-level behavior commands and the trajectory planner. There are two challenges in the task: 1) Analyzing the surrounding vehicles' mutual effects from their trajectories. 2) Estimating the proper lane change start point and end point according to the analysis of surrounding vehicles. We propose a learning-based approach to understand surrounding traffic and make decisions for a safe lane change. Our contributions and advantages of the approach are: 1 Considers the behavior generator as a continuous function in Reproducing Kernel Hilbert Space (RKHS) which contains a family of behavior generators; 2 Constructs the behavior generator function in RKHS by non-parametric regressions on training data; 3 Takes past trajectories of all related surrounding cars as input to capture mutual interactions and output continuous values to represent behaviors. Experimental results show that the proposed approach is able to generate feasible and human-like lane-change behavior (represented by start and end points) in multi-car environments. The experiments also verified that our suggested kernel outperforms the ones which were used in a previous method.

Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. In this paper, we present a novel learning-based method to efficiently estimate other vehicles' intentions and interact with them in ramp merging scenarios, without over-the-air communication between vehicles. The intention estimate is generated from a Probabilistic Graphical Model (PGM) which organizes historical data and latent intentions and determines predictions. Real driving trajectories are used to learn transition models in the PGM. Thus, besides the structure of the PGM, our method does not require human-designed reward or cost functions. The PGM-based intention estimation is followed by an off-the-shelf ACC distance keeping model to generate proper acceleration/deceleration commands. The PGM plays a plug-in role in our self-driving framework [1]. We validate the performance of our method both on real merging data and using a designed merging strategy in simulation, and show significant improvements compared with previous methods. Parameter design is also discussed by experiments. The new method is computationally efficient, and does not require acceleration information about other vehicles, which is hard to read directly from sensors mounted on the autonomous vehicle.

The detection of surrounding vehicles is an essential task in autonomous driving, which has been drawing enormous attention recently. When using laser scanners, L-Shape fitting is a key step for model-based vehicle detection and tracking, which requires thorough investigation and comprehensive research. In this paper, we formulate the L-Shape fitting as an optimization problem. An efficient search based method is then proposed to find the optimal solution. Our method does not rely on laser scan sequence information and therefore supports convenient data fusion from multiple laser scanners; it is efficient and involves very few parameters for tuning; the approach is also flexible to suit various fitting demands with different fitting criteria. On-road experiments with production-grade laser scanners have demonstrated the effectiveness and robustness of our approach.

This paper investigates real-time on-road motion planning algorithms for autonomous passenger vehicles (APV) in urban environments, and propose a computationally efficient planning formulation. Two key properties, tunability and stability, are emphasized when designing the proposed planner. The main contributions of this paper are: · A computationally efficient decoupled space-time trajectory planning structure. · The formulation of optimization-free elastic-band-based path planning and speed-constraint-based temporal planning routines with pre-determined runtime. · Identification of continuity problems with previous cost-based planners that cause tunability and stability issues.