May Mobility

On April [XXX], 2026, at 6:28 PM ET, a passenger car collided with a May Mobility Autonomous Vehicle (AV) operating in Atlanta, Georgia on [XXX] near [XXX]. The May Mobility AV was traveling east on [XXX] in the left lane. A passenger car traveling east on [XXX] in the right lane proceeded to change lanes from the right lane to the left lane. The May Mobility AV Operator began braking before a ped

On March [XXX], 2026, at 10:18 am, a third-party vehicle collided with a May Mobility Autonomous Vehicle (Ego) in Arlington, Texas, at the intersection of [XXX] and [XXX]. The May Ego was traveling north on [XXX], making a right turn at a yield sign onto eastbound [XXX]. with its automated driving system engaged in autonomy mode. The May Ego stopped at the yield sign then held its position in orde

The May Mobility vehicle (Ego) was operating in autonomy while waiting in slow traffic in the leftmost of two parallel lanes. Ahead, both lanes were obstructed by stopped vehicles, prompting traffic to move to the center turn lane to proceed. The Autonomous Vehicle Operator (AVO) intervened to move closer to the lead vehicle as another vehicle in the right lane attempted to merge left, preventing

The May Mobility vehicle (Ego) is stopped at a red light, then the Ego begins to accelerate and turn right when the traffic light changes to green. As the Ego accelerates, a pedestrian begins moving and enters the crosswalk from the other side of the intersection. This activates the rule-based crosswalk logic. Ego determines that it is able to stop before the crosswalk without exceeding our max de

The May Mobility vehicle (Ego) approaches a turn with a trailing vehicle following closely behind. The May planner initially selects a trajectory it believes will safely complete the turn. However, as the Ego begins the turn, it re-evaluates the situation and detects a significant risk of striking the curb. At that point, the planner shifts focus from avoiding the curb entirely to minimizing the s

During normal autonomous operation, the May autonomous shuttle (Ego) was traveling through a narrow section of roadway in a parking lot bordered by parked vehicles on the left and a retaining wall on the right on experimental software version v29.0.20. As the Ego proceeded, the system initially recognized the static retaining wall as a large, non-rectangular object. As the Ego approaches the wall,

The May autonomous shuttle (Ego) stopped in Autonomous mode safely behind a lead vehicle at a traffic light, which then, the lead vehicle, reversed into the stationary Ego. The Autonomous vehicle operator took over control of the vehicle and attempted to reverse, but was unable to get out of the way of the reversing vehicle. The planner did roll out the agent with a reversing policy and predicted

A May autonomous shuttle (Ego) is approaching a passenger stop location in autonomy. The vehicle operator (AVO) presses on the accelerator, taking control of the Ego, then accelerates into the curb at the stop. The Ego made contact with the curb 3 seconds after switching into manual, causing severe damage to the front-right tire. Resimulation of this event shows that there were no autonomy errors

A May autonomous shuttle (Ego) is passing through an intersection. A vehicle sensor detects a false positive as a pedestrian moving toward the May vehicle€™s path in a region closely behind another vehicle. This causes the May vehicle to stop to allow it to safely pass. A following agent (still accelerating at the time of the impact) collides with the May vehicle one second after we come to a s

The May autonomous vehicle was travelling in an autonomous vehicle (AV) only lane, on [XXX], approaching the intersection at [XXX] A vehicle travelling to the left of the May shuttle in the non-AV lane attempted to perform a right hand turn across the May vehicles path and collided with the May AV. The primary factor that led to this collision was human error on the part of the other vehicle tha