论文标题
自主赛车的端到端速度估计
End-to-End Velocity Estimation For Autonomous Racing
论文作者
论文摘要
速度估计在无人驾驶车辆中起着核心作用，但是由于存在高侧光，标准和负担得起的方法难以应对极端情况。为了解决这个问题，自动赛车通常配备昂贵的外部速度传感器。在本文中，我们提出了一个端到端的复发性神经网络，该网络将可用的原始传感器作为输入（IMU，车轮频能计和电动机电流），并输出速度估计值。将结果与两个最先进的卡尔曼过滤器进行了比较，分别包括和排除昂贵的速度传感器。所有方法均已在公式的无人驾驶赛车上进行了广泛的测试，其侧轴非常高（后桥为10°）和滑动比（〜20％），接近处理范围。所提出的网络能够估计具有等效传感器输入的卡尔曼滤波器的横向速度高达15倍，并匹配（0.06 m/s rmse），带有昂贵的速度传感器设置的卡尔曼滤波器。
Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip. To solve this, autonomous race cars are usually equipped with expensive external velocity sensors. In this paper, we present an end-to-end recurrent neural network that takes available raw sensors as input (IMU, wheel odometry, and motor currents) and outputs velocity estimates. The results are compared to two state-of-the-art Kalman filters, which respectively include and exclude expensive velocity sensors. All methods have been extensively tested on a formula student driverless race car with very high sideslip (10° at the rear axle) and slip ratio (~20%), operating close to the limits of handling. The proposed network is able to estimate lateral velocity up to 15x better than the Kalman filter with the equivalent sensor input and matches (0.06 m/s RMSE) the Kalman filter with the expensive velocity sensor setup.
