Are self-driv­ing cars the fu­ture of mo­bil­ity for dis­abled peo­ple?

Manteca Bulletin - - On The Road - Texas A&M Univer­sity

Self-driv­ing cars could rev­o­lu­tion­ize how dis­abled peo­ple get around their com­mu­ni­ties and even travel far from home. Peo­ple who can’t see well or with phys­i­cal or men­tal dif­fi­cul­ties that pre­vent them from driv­ing safely of­ten rely on oth­ers – or lo­cal govern­ment or non­profit agen­cies – to help them get around.

Au­ton­o­mous ve­hi­cle tech­nol­ogy on its own is not enough to help th­ese peo­ple be­come more in­de­pen­dent, but si­mul­ta­ne­ous ad­vances in ma­chine learn­ing and ar­ti­fi­cial in­tel­li­gence can en­able th­ese ve­hi­cles to un­der­stand spo­ken in­struc­tions, ob­serve nearby sur­round­ings and com­mu­ni­cate with peo­ple. To­gether, th­ese tech­nolo­gies can pro­vide in­de­pen­dent mo­bil­ity with prac­ti­cal as­sis­tance that is spe­cial­ized for each user’s abil­i­ties and needs.

A lot of the nec­es­sary tech­nol­ogy al­ready ex­ists, at least in pre­lim­i­nary forms. Google has asked a blind per­son to test its au­ton­o­mous ve­hi­cles. And Microsoft re­cently re­leased an app called “See­ing AI” that helps vis­ually im­paired peo­ple bet­ter sense and un­der­stand the world around them. “See­ing AI” uses ma­chine learn­ing, nat­u­ral lan­guage pro­cess­ing and com­puter vi­sion to un­der­stand the world and de­scribe it in words to the user.

In the lab I run at Texas A&M, along with the Texas A&M Trans­porta­tion In­sti­tute, we are de­vel­op­ing pro­to­cols and al­go­rithms for peo­ple with and with­out dis­abil­i­ties and au­ton­o­mous ve­hi­cles to com­mu­ni­cate with each other in words, sound and on elec­tronic dis­plays. Our self-driv­ing shut­tle has given rides to 124 peo­ple, to­tal­ing 60 miles of travel. We are find­ing that this type of ser­vice would be more help­ful than cur­rent trans­porta­tion op­tions for dis­abled peo­ple. Para­tran­sit to­day Un­der the Amer­i­cans with Dis­abil­i­ties Act of 1990, all pub­lic tran­sit agen­cies must of­fer trans­porta­tion ser­vices to peo­ple with phys­i­cal hand­i­caps, vis­ual or men­tal con­di­tions or in­juries that pre­vent them from driv­ing on their own. In most com­mu­ni­ties, this type of trans­port, typ­i­cally called “para­tran­sit,” is sort of like an ex­tra-help­ful taxi ser­vice run by pub­lic tran­sit. Rid­ers make reser­va­tions in ad­vance for rides to, say, gro­cery stores and med­i­cal ap­point­ments. The ve­hi­cles are usu­ally wheel­chair-ac­ces­si­ble and are driven by trained op­er­a­tors who can help rid­ers board, find seats and get off at the right stop.

Like taxis, para­tran­sit can be costly. A Govern­ment Ac­count­abil­ity Of­fice re­port from 2012 pro­vides the only re­li­able na­tion­wide es­ti­mates. Those num­bers sug­gest that per trip, para­tran­sit costs three to four times what mass tran­sit costs. And the costs are in­creas­ing, as are the num­ber of peo­ple need­ing to use para­tran­sit. At the same time, fed­eral, state and lo­cal fund­ing for tran­sit au­thor­i­ties has stag­nated.

In an at­tempt to meet some of the de­mand, many com­mu­ni­ties have re­duced the geo­graphic ar­eas where para­tran­sit is avail­able and asked dis­abled peo­ple to use mass tran­sit when pos­si­ble. Other places have ex­per­i­mented with on-de­mand ride-hail­ing ser­vices like Uber and Lyft. But in many cases the driv­ers are not trained to help dis­abled peo­ple, and the ve­hi­cles are not usu­ally wheel­chair-ac­ces­si­ble or oth­er­wise suit­able for cer­tain rid­ers. A pos­si­ble so­lu­tion Au­ton­o­mous shut­tles, like the one we’re test­ing on the Texas A&M cam­pus, can be a so­lu­tion for th­ese problems of ac­cess and fund­ing. We en­vi­sion a fully in­te­grated sys­tem in which users can con­nect to the dis­patch­ing sys­tem and cre­ate pro­files that in­clude in­for­ma­tion on their dis­abil­i­ties and com­mu­ni­ca­tions pref­er­ences as well as any par­tic­u­lar fre­quent des­ti­na­tions for trips (like a home ad­dress or a doc­tor’s of­fice).

Then, when a rider re­quests a shut­tle, the sys­tem would dispatch a ve­hi­cle that has any par­tic­u­lar equip­ment the rider needs, like a wheel­chair ramp or ex­tra room, for in­stance, to al­low a ser­vice dog to travel.

When the shut­tle ar­rives to pick up the rider, it could scan the area with lasers, cam­eras and radar to cre­ate a 3-D map of the area, merg­ing those data with traf­fic and geo­graphic in­for­ma­tion from var­i­ous on­line sources like Google Maps and Waze. Based on all of those data, it would de­ter­mine an ap­pro­pri­ate board­ing spot, iden­ti­fy­ing curb cuts that let wheel­chairs and walk­ers pass eas­ily as well as not­ing po­ten­tial ob­sta­cles, like trash cans out for col­lec­tion.

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