Reinforcement learning (RL) methods are commonly regarded as effective solutions for designing intelligent driving policies. Nonetheless, even if the RL policy is converged after training, it is notoriously difficult to ensure safety. In particular, RL policy is susceptible to insecurity in the presence of long-tail or unseen traffic scenarios, i.e. , out-of-distribution test data. Therefore, the design of the RL-based decision-making method must account for this shift in distribution. This paper proposes a robust decision-making framework for autonomous driving on the highway to improve driving safety. First, a Deep Deterministic Policy Gradient (DDPG)-based RL policy that directly maps observations to actions is constructed. Subsequently, the model uncertainty of the DDPG policy is evaluated at runtime to quantify the policy's reliability and identify unseen scenarios. In addition, a complementary principle-based policy is developed using the intelligent driver model (IDM) and the model for minimizing overall braking induced by lane changes (MOBIL). It will take over the DDPG policy when encountering unseen scenarios to guarantee a lower-bound performance of the decision-making system. Finally, the proposed method is implemented on an embedded system, i.e. , NVIDIA Jetson AGX Xavier, and out-of-training distribution challenging cases are considered in the experiment, i.e. , observation with sensor noise, traffic density increasing significantly, objects falling from the front vehicle, and road construction causing temporal changes in road structure. Results indicate that the proposed framework outperforms state-of-the-art benchmarks. Additionally, the code is provided https://github.com/Kayne0401/Robust-Decision-Making-Framework .

Foot-mounted inertial navigation is an important issue in areas such as pedestrian localization, gait analysis, and sport training. However, low-cost inertial sensors suffer from several errors that make the navigation results less convincing. In this paper, a multi-sensor approach with one sensor on each foot is presented to reduce the system heading drift. Through dual-gait analysis, gait parameters between two feet are employed to make the non-collocated and uncoupled subsystems be related to each other. A step length estimator based on an inverted pendulum model is developed to derive a relative position vector between the two foot-mounted sensors rather than a distance scalar. A Kalman-type filter with one time update and two measurement updates is developed to fuse the velocity and position observations at foot and person levels, respectively. Experiments were conducted by four healthy subjects, and experimental results show that the proposed sensor fusion method can effectively reduce the heading drift of inertial navigation and make the captured dual-foot motion closer to its actual process.

Wearable inertial motion capture, a new type of motion capture technology, mainly estimates the human posture in 3-D space through multisensor data fusion. The available method for sensor fusion is usually aided by magnetometers to remove the drift error in yaw angle estimation, which in turn limits their application in the presence of a complex magnetic field environment. In this article, an extended Kalman filter (EKF) data fusion method is proposed to fuse the 9-axis sensor data. Meanwhile, the heuristic drift reduction (HDR) method is used to calibrate the accumulated error of a heading angle. In addition, the position in 3-D space is estimated by the foot-mounted zero-velocity-update (ZUPT) technique. Combining 3-D attitude and position, a biomechanical model of the human body is established to track the motion of a real human body. The EKF algorithm and position estimation methods are benchmarked against the golden standard, optical motion capture system, for various indoor experiments. In addition, various outdoor experiments are also conducted to verify the reliability of the proposed method. The results show that the proposed algorithm outperforms the available attitude estimation model in motion tracking and is feasible for 3-D human motion capture.