A safety device according to the present disclosure includes a sensor that is attached to a self-propellable travel device or a robot provided to the travel device, is set with a given detection area on the basis of a position of the sensor, and detects an object existing within the given detection area. The safety device further includes a motion suppressing device that suppresses motions of the travel device and the robot, when the existence of the object within the given detection area is detected by the sensor, and an area changing device that changes the given detection area according to operating states of the travel device and the robot.

A vehicle includes an on-board controller. A position of the vehicle is detected by a positioning sensor and a CPU of the on-board controller Based on the position of the vehicle and area information stored in a memory, the CPU determines whether the vehicle is in a free driving zone, an alternative driving zone, or a remote driving zone. The CPU sets a driving mode to either one of a free driving mode, an alternative driving mode, and a remote driving mode based on a signal of the driving mode of the vehicle inputted by a main key of a vehicle main body and information indicating which driving zone the vehicle is in. A CPU of the vehicle main body controls an operation of the vehicle in accordance with the set driving mode. The on-board controller may be provided by a mobile terminal.

Transporters and methods for transporting an object. The transporter includes a carrier comprising a plurality of coupling members; a support assembly adapted to support the carrier; and a plurality of automatic guided vehicles configured to obtain kinematic information from a leading automatic guided vehicle of the plurality of automatic guided vehicles. Each of the plurality of automatic guided vehicles includes a carrier connecting member coupled to the respective coupling member of the carrier to enable the carrier to move with the plurality of automatic guided vehicles; and a patrol assembly adapted to enable the respective automatic guided vehicle to move along the predetermined path.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

In some embodiments, methods and systems are provided that provide for facilitating delivery, via autonomous ground vehicles, of products ordered by customers of a retailer to customer-specified restricted areas accessible by an entryway openable via an access code.

In various examples, a sequential deep neural network (DNN) may be trained using ground truth data generated by correlating (e.g., by cross-sensor fusion) sensor data with image data representative of a sequences of images. In deployment, the sequential DNN may leverage the sensor correlation to compute various predictions using image data alone. The predictions may include velocities, in world space, of objects in fields of view of an ego-vehicle, current and future locations of the objects in image space, and/or a time-to-collision (TTC) between the objects and the ego-vehicle. These predictions may be used as part of a perception system for understanding and reacting to a current physical environment of the ego-vehicle.

In one embodiment, when an autonomous driving vehicle (ADV) is parked, the ADV can determine, based on criteria, whether to operate in an open-space mode or an on-lane mode. The criteria can include whether the ADV is within a threshold distance and threshold heading relative to a vehicle lane. If the criteria are not satisfied, then the ADV can enter the open-space mode. While in the open-space mode, the ADV can maneuver it is within the threshold distance and the threshold heading relative to the vehicle lane. In response to the criteria being satisfied, the ADV can enter and operate in the on-lane mode for the ADV to resume along the vehicle lane.

An example operation includes one or more of receiving, by a server, data related to an environment associated with a target transport, analyzing, by the server, the data to determine if at least one adverse condition related to the environment exists, and responsive to existence of the at least one adverse condition, sending, by the server, a recommendation related to operation of the target transport in a safe mode to overcome the at least one adverse condition to the target transport.

A set of input conditions is obtained. A plurality of potential decisions is obtained based at least in part on the set of input conditions. A rule-based system is used to process the plurality of potential decisions and obtain a set of one or more updated potential decisions, wherein: the rule-based system specifies a plurality of rules; a rule specifies a rule condition and a corresponding action, wherein when the rule condition is met, the corresponding action is to be performed; and using the rule-based system to process the plurality of potential decisions includes: for a selected potential decision in the plurality of potential decisions, determining whether the rule condition is met for a selected rule among the plurality of rules, wherein the selected rule condition is dependent on, at least in part, the selected potential decision; and in response to the selected rule condition being met, performing the corresponding action. The set of one or more updated potential decisions to be executed is output.

Systems and methods are provided for implementing hybrid prediction. Hybrid prediction integrates two deep learning based trajectory prediction approaches: grid-based approaches and graph-based approaches. Hybrid prediction techniques can achieve enhanced performance by combining the grid and graph approaches in a manner that incorporates appropriate inductive biases for different elements of a high-dimensional space. A hybrid prediction framework processor can generate trajectory predictions relating to movement of agents in a surrounding environment based on a prediction model generating using hybrid prediction. Trajectory predictions output from the hybrid prediction framework processor can be used to control an autonomous vehicle. For example, the autonomous vehicle can perform safety-aware and autonomous operations to avoid oncoming objects, based on the trajectory predictions.