Adverse weather has raised a big challenge for autonomous vehicles. Unreliable measurements due to sensor degradation could seriously affect the performance of autonomous driving tasks, such as perception and localization. In this work, we study sensor degradation in rainy weather and present a novel method that evaluates the uncertainty for each laser measurement from a 3D LiDAR. With uncertainty estimation, downstream tasks that rely on LiDAR input (e.g., perception or localization) can increase their reliability by adjusting their reliance on laser measurements with varying fidelity. Alternatively, uncertainty estimation can be used for sensor performance evaluation. Our proposed method, SmartRainNet, uses an attention-based Mixture Density Network to model the dependence between neighboring laser measurements and then calculate the probability density for each laser measurement as an uncertainty score. We evaluate SmartRainNet on synthetic and naturalistic sensor degradation datasets and provide qualitative and quantitative results to demonstrate the effectiveness of our method in evaluating uncertainty. Finally, we demonstrate three practical applications of uncertainty estimation to address autonomous driving challenges in rainy weather.

Autonomous driving in rain remains challenging. Rain causes sensor performance degradation that can affect sensor measurement quality. During the rain, lasers may suffer from energy loss due to raindrop absorption. As a result, some laser measurements reflected from obstacles may not be recognized by the LiDAR sensor, thus raising potential risks for autonomous vehicles. This work investigates a novel task that aims to detect those missing measurements. Our solution uses a two-stage learning method to generate an anomaly score for each missing measurement, representing the likelihood of being caused by rain. We evaluate our method with real-world data and demonstrate its effectiveness in identifying anomalous missing measurements through qualitative and quantitative experiments.

Autonomous driving in the rain remains a challenge. One main problem is performance degradation caused by rain. This work introduces a new dataset to study this problem. Our dataset is collected from a full-scale vehicle equipped with a 3D LiDAR sensor and multiple forward-facing cameras under various rainy conditions. In addition, rainfall intensity is recorded in real-time from a rain sensor. The combination of sensor and rainfall intensity measurement is designed for studying algorithm performance under different levels of rainfall. In this work, in addition to presenting dataset creation details, we also introduce three degradation evaluation tasks with baseline results, including rainfall intensity estimation, LiDAR degradation estimation, and 2D object detection evaluation. This dataset, development kit, and baseline codes will be made available at https://smart-rain-dataset.github.io/

Sensor degradation is one of the major challenges for autonomous driving. During the rain, the interference from raindrops can negatively influence LiDAR measurements. For example, valid measurements could be reduced during the rain, and some measurements may become noisy. Unreliable measurements can lead to potential safety issues if autonomous driving systems are unaware of these changes. In this work, we will release a naturalistic driving dataset to advance the research in studying LiDAR degradation. Our dataset consists of 3D LiDAR scans collected by a data collection vehicle under various rainy conditions. Besides these raw scans, we also release LiDAR scan pairs (each pair consists of one scan from rainy weather and one scan from clear weather at the same location). These LiDAR pairs are developed to help researchers identify LiDAR degradation. Finally, we will release a toolbox integrated with mapping, localization, and scan synthesis functions used to create this dataset. This toolbox can facilitate dataset creation for studying degradation in other harsh weather conditions. More information can be found at https://smart-rain-dataset.github.io/.

Navigation through dynamic pedestrian environments in a socially compliant manner is still a challenging task for autonomous vehicles. Classical methods usually lead to unnatural vehicle behaviours for pedestrian navigation due to the difficulty in modeling social conventions mathematically. This paper presents an end-to-end path planning system that achieves autonomous navigation in dynamic environments through imitation learning. The proposed system is based on a fully convolutional neural network that maps the raw sensory data into a confidence map for path extraction. Additionally, a classification network is introduced to reduce the unnecessary re-plannings and ensures that the vehicle goes back to the global path when re-planning is not needed. The imitation learning based path planner is implemented on an autonomous wheelchair and tested in a new real-world dynamic pedestrian environment. Experimental results show that the proposed system is able to generate paths for different driving tasks, such as pedestrian following, static and dynamic obstacles avoidance, etc. In comparison to the state-of-the-art method, our system is superior in terms of generating human-like trajectories.

Autonomous driving in rainy conditions remains a big challenge. One of the issues is sensor degradation. LiDAR is commonly used in autonomous driving systems to perceive and understand surrounding environments. However, LiDAR performance can be degraded by rain, thereby influencing other system performance (e.g., perception or localization). Therefore, knowing how much degradation exists in current LiDAR measurements is necessary. Most existing methods can only measure LiDAR degradation in controlled environments (e.g., a chamber with simulated rain); how to quantify LiDAR degradation in dynamic environments while the autonomous vehicle is moving is still a difficult problem. In this work, we propose a novel approach to address this problem using an anomaly detection method. Our method has been evaluated on simulated and real-world data. Experimental results demonstrate the effectiveness of our method to capture LiDAR degradation and yield reasonable degradation estimations. Our experimental data and codes are accessible from http://rain.smart.mit.edu/smartrain/.

We present a real-time vehicle detection and tracking system to accomplish the complex task of driving behavior analysis in urban environments. We propose a robust fusion system that combines a monocular camera and a 2D Lidar. This system takes advantage of three key components: robust vehicle detection using deep learning techniques, high precision range estimation from Lidar, and road context from the prior map knowledge. The camera and Lidar sensor fusion, data association and track management are all performed in the global map coordinate system by taking into account the sensors' characteristics. Lastly, behavior reasoning is performed by examining the tracked vehicle states in the lane coordinate system in which the road context is encoded. We validated our approach by tracking a leading vehicle while it performed usual urban driving behaviors such as lane keeping, stop-and-go at intersections, lane changing, overtaking and turning. The leading vehicle was tracked consistently throughout the 2.3 km route and its behavior was classified reliably.

This paper introduces a map-based localization method aiming to increase robustness in rainy conditions. This method utilizes two types of features: ground reflectivity features and vertical features extracted from 3D LIDAR scans and builds vehicle pose belief with two filters: a histogram filter and a particle filter. The posterior distributions from the two filters are integrated to estimate vehicle poses. This method exploits advantages of both features and filters, compensating respective weakness to deal with complex urban environments. Testing was performed in the fair and rainy weather. Road test results prove robustness and reliability of the proposed method.