A weather station system is provided, comprising: a weather station having at least one sensor disposed at an intersection with a traffic cabinet; an interface card adapted for insertion in a detection rack of a traffic cabinet. The card has embedded firmware to receive data from the at least one sensor of the weather station and communicate the data to at least one remote computing device for processing with data of other weather stations. A weather data processing system and a method of retrieving localized weather data are also provided.

Example systems disclosed herein include a temporary traffic control (TTC) zone monitoring system that assesses and responds to a risk of an adverse event in the TTC zone. The TTC zone monitoring system includes at least one sensor and a computing device. The computing device processes sensor data from the sensor to identify one or more features of the TTC zone. The features of the TTC zone include traffic control features, vehicle features, pathway features, or environmental features. The computing device determines a risk indicative of a risk of an adverse event in the TTC zone based on risk factors. Each of the risk factors is indicative of a risk of an adverse event associated with at least one feature the TTC zone and is based on adverse event data of the at least one feature. The computing device performs at least one operation based on the risk score.

An example method includes receiving, from one or more sensors associated with an autonomous vehicle, sensor data associated with a target object in an environment of the vehicle during a first environmental condition, where at least one sensor of the sensor(s) is configurable to be associated with one of a plurality of operating field of view volumes. The method also includes based on the sensor data, determining at least one parameter associated with the target object. The method also includes determining a degradation in the parameter(s) between the sensor data and past sensor data, where the past sensor data is associated with the target object in the environment during a second environmental condition different from the first and, based on the degradation, adjusting the operating field of view volume of the at least one sensor to a different one of the operating field of view volumes.

An example method includes receiving, from one or more sensors associated with an autonomous vehicle, sensor data associated with a target object in an environment of the vehicle during a first environmental condition, where at least one sensor of the sensor(s) is configurable to be associated with one of a plurality of operating field of view volumes. The method also includes based on the sensor data, determining at least one parameter associated with the target object. The method also includes determining a degradation in the parameter(s) between the sensor data and past sensor data, where the past sensor data is associated with the target object in the environment during a second environmental condition different from the first and, based on the degradation, adjusting the operating field of view volume of the at least one sensor to a different one of the operating field of view volumes.

A method and apparatus for identifying a traffic accident, a device and a computer storage medium are disclosed, which relates to the technical fields of intelligent traffic and big data. An implementation includes: acquiring road features, environmental features and road traffic stream features; inputting the road features, the environmental features and the road traffic stream features into a pre-trained traffic-accident identifying model to obtain an accident-information identifying result of a road which at least includes an accident identifying result. In the present application, the accident road may be automatically identified according to the road features, the environmental features and the road traffic stream features. Compared with a traditional manual reporting way, timeliness is stronger, and a coverage rate is higher.

A method and apparatus for identifying a traffic accident, a device and a computer storage medium are disclosed, which relates to the technical fields of intelligent traffic and big data. An implementation includes: acquiring road features, environmental features and road traffic stream features; inputting the road features, the environmental features and the road traffic stream features into a pre-trained traffic-accident identifying model to obtain an accident-information identifying result of a road which at least includes an accident identifying result. In the present application, the accident road may be automatically identified according to the road features, the environmental features and the road traffic stream features. Compared with a traditional manual reporting way, timeliness is stronger, and a coverage rate is higher.

A method for risk based processing, the method may include detecting, based on first sensed information sensed at a first period, a suspected risk within an environment of a vehicle; selecting, from reference information, a situation related subset of the reference information, wherein the situation related subset of the reference information is related to the situation; selecting, from reference information, a suspected risk related subset reference information, wherein the potential risk related subset of the reference information is related to the potential risk; and determining whether the suspected risk is an actual risk, based at least on part on the suspected risk related subset reference information.

Various arrangements for determining that a vehicle is present in a parking space are presented. A parking sensor can make a plurality of magnetic field strength measurements using a magnetometer. A determination that the vehicle is parked proximate to the parking sensor device within the parking space can be based on a magnetic field strength measurement in relation to the selected magnetic field strength window.

An ADV includes a vehicle frame housing the ADV, a first LIDAR, a second LIDAR, a third LIDAR devices, and an autonomous driving system responsible for autonomously driving the ADV. The first LIDAR device is mounted on a frontend of the vehicle frame. The second LIDAR device is mounted on a first side (e.g., left side) of the vehicle frame and the third LIDAR device is mounted on a second side (e.g., right side) of the vehicle frame. The autonomous driving system includes a perception module and a planning module. The perception module is configured to perceive a driving environment surrounding the ADV at least based on LIDAR data obtained from the first, second, and third LIDAR devices. The planning module is configured to plan a path to drive the ADV based on perception data from the perception module representing the driving environment. The ADV may be an autonomous driving bus.

An ADV includes a vehicle frame housing the ADV, a first LIDAR, a second LIDAR, a third LIDAR devices, and an autonomous driving system responsible for autonomously driving the ADV. The first LIDAR device is mounted on a frontend of the vehicle frame. The second LIDAR device is mounted on a first side (e.g., left side) of the vehicle frame and the third LIDAR device is mounted on a second side (e.g., right side) of the vehicle frame. The autonomous driving system includes a perception module and a planning module. The perception module is configured to perceive a driving environment surrounding the ADV at least based on LIDAR data obtained from the first, second, and third LIDAR devices. The planning module is configured to plan a path to drive the ADV based on perception data from the perception module representing the driving environment. The ADV may be an autonomous driving bus.