An information processing apparatus includes a controller configured to execute the processing of finding, in connection with a first vehicle whose cabin unit and travel unit are separable from each other and that is scheduled to travel a first section of road in which travelling with a travel unit of a first type is suitable and provided with a travel unit of a second type different from the first type, a second vehicle that is scheduled to travel the first section in the direction opposite to the direction of travel of the first vehicle and finish travelling the first section and provided with a travel unit of the first type, and instructing the first vehicle and the second vehicle to exchange the travel unit of the first type and the travel unit of the second type.

According to some embodiments, there is provided a Collective Perception Message, CPM, characterizing a plurality of Vulnerable Road Users based on a plurality of received VAMs, thereby allowing an ITS station to efficiently aggregate VAM messages from VRUs and retransmit information about the VRUs to other ITS stations. Consequently, the security is improved as some ITS stations may not be able to detect or identify VRU stations by themselves but thanks to the CPM, these stations can still be informed of the VRUs. According to other aspects, congestion is avoided while maintaining safety vis-à-vis VRUs thanks to the use of a different transmission scheme when the VRU is already characterized in a CPM sent to the ITS stations. Also, a receiving station can evaluate whether the content of a CPM can be trusted or not. Safety is thus improved. This is achieved thanks to the CPM that references a certificate.

A processing apparatus for traffic management of a network of roads is provided, to, process data corresponding to the network of roads to identify an incoming road and an outgoing road intersecting at an intersection node of the network, the incoming road being for incoming traffic leading to the intersection node and the outgoing road being for outgoing traffic leading away from the intersection node, determine, based on the data corresponding to the network, whether there is a bypass road to allow the incoming traffic from the incoming road to bypass the intersection node and flow to the outgoing road via the bypass road, and, if it is determined that there is the bypass road, generate data indicative of a turn restriction for communicating to road users of restriction of flow of the incoming traffic to the outgoing road via the intersection node.

A collision avoidance system includes: an onboard acquisition unit provided in a vehicle traveling on a road that acquires state quantity data indicating a state quantity of a pneumatic tire of the vehicle; an onboard transmission unit provided in the vehicle that transmits the state quantity data acquired by the onboard acquisition unit to a data acquisition roadside device installed on the road; an abnormality determination unit that determines whether or not the state quantity data is abnormal; and a management device including a data acquisition unit that acquires the state quantity data from the data acquisition roadside device, a data storage unit that stores the state quantity data acquired by the data acquisition unit, and a data distribution unit that distributes the state quantity data determined as abnormal by the abnormality determination unit.

Electronic digital display systems, including roadside display devices, vehicle-based devices, personal mobile devices, intermediary servers, advertising servers, and/or additional external data sources may operate individually or in combination to identify one or more vehicle locations, driving routes, driver and passenger characteristics, and the like. Vehicle and individual characteristics may be determined based on data received from traffic cameras, vehicle-based devices, personal mobile devices, and/or other data sources. Based on the vehicle characteristics, individual characteristics, driving data and driving patterns, and the like, digital content may be determined for electronic roadside displays to be viewable by the approaching vehicles, and/or other digital display devices to be viewable by associated individuals via other display devices and at other times. Various techniques may be used to determine customized digital content. Additionally, certain systems may be interactive to allow user responses and follow-up content via on-board vehicle devices or other user devices.

A computer-implemented method of updating an auto insurance policy is provided. The method may include (1) determining that a customer's mobile device has a Telematics Application (“App”) installed on it, the Telematics App configured to (i) receive telematics data associated with another vehicle via a wireless communication broadcast; (ii) determine a travel event from analysis of the telematics data received, and (iii) generate a corrective action based upon the telematics data received or travel event determined that alleviates the risk of vehicle collision. The method may also include (2) monitoring, with the customer's permission, an amount or percentage of usage of the Telematics App on the customer's mobile device while the customer is driving in an insured vehicle; and (3) adjusting an insurance policy premium or discount based upon usage of the Telematics App to facilitate rewarding risk-averse drivers and encourage usage of risk mitigation or prevention technology.

A non-transitory, computer-readable recording medium stores therein a trigger condition determining program that causes a computer to execute a process including distributing and allocating to plural vehicle groups, a pattern candidate when plural pattern candidates of a trigger condition for control in a vehicle corresponding to driving operation is present, the pattern candidate being each pattern candidate of the plural pattern candidates; evaluating the trigger condition corresponding to the allocated pattern candidate, based on a change in travel information before and after an application of the trigger condition corresponding to the allocated pattern candidate; and setting among plural trigger conditions, a trigger condition having a relatively high evaluation or satisfying a predetermined standard to be a trigger condition that is to be applied in a service provided to the plural vehicle groups.

Techniques for determining that a first vehicle is associated with a reverse state, and controlling a second vehicle based on the reverse state, are described herein. In some examples, the first vehicle may provide an indication that the first vehicle will be executing a reverse maneuver, such as with reverse lights on the vehicle or by positioning at an angle relative to a road or parking space to allow for the reverse maneuver into a desired location. A planning system of the second vehicle (such as an autonomous vehicle) may receive sensor data and determine a variety of these indications to determine a probability that the vehicle is going to execute a reverse maneuver. The second vehicle can further determine a likely trajectory of the reverse maneuver and can provide appropriate accommodations (e.g., time and/or space) to allow the second vehicle to execute the maneuver safely and efficiently.

Systems and methods for dynamically reprioritizing pick jobs in a fulfillment center are described herein. The example systems can be configured to periodically classify pick jobs in order to optimize throughput of the fulfillment center. The classifying can include determining an estimated completion time for pick jobs and identifying at-risk jobs that may complete after their associated due dates. The at-risk jobs can be assigned to autonomous vehicles based primarily on their associated due dates. Other pick jobs that are not at-risk can be assigned to autonomous vehicles based primarily on efficiency. The at-risk pick jobs can be assigned to autonomous vehicles before the other pick jobs.

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.