Systems and methods for personalizing adaptive cruise control in a vehicle are disclosed herein. One embodiment collects vehicle-following-behavior data associated with a particular driver; trains a Gaussian Process (GP) Regression model using the collected vehicle-following-behavior data to produce a set of adaptive-cruise-control (ACC) parameters pertaining to the particular driver, the set of ACC parameters modeling learned vehicle-following behavior of the particular driver; generates an acceleration command for the vehicle based, at least in part, on the set of ACC parameters; applies a predictive safety filter to the acceleration command to produce a certified acceleration command that has been vetted for safety; and controls acceleration of the vehicle automatically in accordance with the certified acceleration command to regulate a following distance between a lead vehicle and the vehicle in accordance with the learned vehicle-following behavior of the particular driver.

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).

This document describes techniques for using occlusion constraints for resolving tracks from multiple types of sensors. In aspects, an occlusion constraint is applied to an association between a radar track and vision track to indicate a probability of occlusion. In other aspects, described are techniques for a vehicle to refrain from evaluating occluded radar tracks and vision tracks collected by a perception system. The probability of occlusion is utilized for deemphasizing pairs of radar tracks and vision tracks with a high likelihood of occlusion and therefore, not useful for tracking. The disclosed techniques may provide improved perception data more closely representing multiple complex data sets for a vehicle for preventing a collision with an occluded object as the vehicle operates in an environment.

A system and method of detecting and mitigating an erratic vehicle by a host vehicle. The method includes gathering sensor information on a calibratable external region surrounding the host vehicle; analyzing the sensor information to detect a target vehicle traveling in a lane and a movement of the target vehicle in the lane; determining whether the movement of the target vehicle in the lane is erratic; if erratic then designating target vehicle as erratic vehicle; assigning a risk score to the erratic vehicle; and implementing a predetermined mitigating action correlating to the assigned risk score to the erratic vehicle. The mitigating action includes one or more of: warning an operator of the host vehicle, warning a vehicle proximal to the host vehicle, and taking at least partial control of the host vehicle to further distance the host vehicle apart from the erratic vehicle.

A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

A vehicle may receive sensor data captured by a sensor of the vehicle, determine that the sensor data represents an object in the environment, and determine an impact location between the vehicle and the object. The impact location may be associated with a predicted collision between the vehicle and the object. The vehicle may also determine an object type corresponding to the object and/or a characteristic of the object. Based at least in part on the impact location, object type, and/or the characteristic, a ride height of the vehicle may be adjusted.

A system includes a processor and a memory storing instructions executable by the processor to control at least one of a steering system or a propulsion system to operate a vehicle at a speed below a speed threshold. The instructions include instructions to determine whether one or more second vehicles a first distance from the vehicle are traveling below the speed threshold. The instructions include instructions to, upon determining the second vehicles are traveling below the speed threshold, continue to control the steering system or the propulsion system. The instructions include instructions to, upon determining the second vehicles are not traveling below the speed threshold, transition control of the steering system or the propulsion system to a human operator of the vehicle.

Systems, device, and methods for trajectory association and tracking are provided. A method can include obtaining input data indicative of a respective trajectory for each of one or more first objects for a first time step and input data indicative of a respective trajectory for each of one or more second objects for a second time step subsequent to the first time step. The method can include generating, using a machine-learned model, a temporally-consistent trajectory for at least one of the one or more first objects or the one or more second objects based at least in part on the input data and determining a third predicted trajectory for the at least one of the one or more first objects or the one or more second objects for at least the second time step based at least in part on the temporally-consistent trajectory.

Provided is a notification control apparatus for a vehicle configured to determine, based on surrounding information and an operation state of an operation unit, whether there exists a possibility that an own vehicle turns toward a specific direction while an oncoming vehicle is approaching, to determine, when affirmative determination is made, based on vehicle information including at least one of a steering input value or presence/absence of a brake operation and a vehicle speed, whether a possibility that the own vehicle completes crossing of an opposing lane or an intersection after elapse of a reference period is high, and when affirmative determination is made, to execute the notification control when a virtual passing period required for the own vehicle to virtually pass the oncoming vehicle is a lower limit period or longer and an upper limit period or shorter, the upper limit period being the reference period or shorter.

To provide a technology that makes it possible to recognize a target object quickly and accurately. An information processing apparatus according to the present technology includes a controller. The controller recognizes a target object on the basis of event information that is detected by an event-based sensor, and transmits a result of the recognition to a sensor apparatus that includes a sensor section that is capable of acquiring information regarding the target object.